.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tabular/auto_tutorials/other/plot_phishing_urls.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tabular_auto_tutorials_other_plot_phishing_urls.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tabular_auto_tutorials_other_plot_phishing_urls.py:


Use Cases - Classifying Malicious URLs
**************************************

This notebook demonstrates how the ``deepchecks`` package can help you validate
your basic data science workflow right out of the box!

The scenario is a real business use case: You work as a data scientist at a
cyber security startup, and the company wants to provide the clients with a
tool to automatically detect phishing attempts performed through emails and
warn clients about them. The idea is to scan emails and determine for each
web URL they include whether it points to a phishing-related web page or not.

Since phishing attempts are an always-adapting efforts, static black lists or
white lists composed of good or bad URLs seen in the past are simply not
enough to make a good filtering system for the future. The way the company
chose to deal with this challenge is to have you train a Machine Learning
model to generalize what a phishing URL looks like from historic data!

To enable you to do this the company's security team has collected a set of
benign (meaning OK, or Kosher) URLs and phishing URLs observed during 2019
(not necessarily in clients emails). They have also wrote a script
extracting features they believe should help discern phishing URLs from
benign ones.

These features are divided to three sub-sets:

* String Characteristics - Extracted from the URL string itself.
* Domain Characteristics - Extracted by interacting with the domain provider.
* Web Page Characteristics - Extracted from the content of the web page the
  URL points to.

The string characteristics are based the way URLs are structured, and what
their different parts do. Here is an informative illustration. You can read
more at Mozilla's `What is a URL
<https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_URL>`__
article. We'll see the specific features soon.

.. GENERATED FROM PYTHON SOURCE LINES 42-48

.. code-block:: default


    from IPython.core.display import HTML
    from IPython.display import Image

    Image(url= "https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_URL/mdn-url-all.png")






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <img src="https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_URL/mdn-url-all.png"/>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-56

(Note: This is a slightly synthetic dataset based on `a great project
<https://github.com/Rohith-2/url_classification_dl>`__ by `Rohith Ramakrishnan
<https://www.linkedin.com/in/rohith-ramakrishnan-54094a1a0/>`__ and others,
accompanied by a `blog post
<https://medium.com/nerd-for-tech/url-feature-engineering-and-classification-66c0512fb34d>`__.
The authors has released it under an open license per our request, and for that
we are very grateful to them.)

.. GENERATED FROM PYTHON SOURCE LINES 58-64

**Installing requirements**

.. code:: python

    import sys
    !{sys.executable} -m pip install deepchecks --quiet

.. GENERATED FROM PYTHON SOURCE LINES 66-69

Loading the data
================
OK, let's take a look at the data!

.. GENERATED FROM PYTHON SOURCE LINES 69-78

.. code-block:: default


    import numpy as np
    import pandas as pd
    import sklearn

    import deepchecks

    pd.set_option('display.max_columns', 45); SEED=832; np.random.seed(SEED);








.. GENERATED FROM PYTHON SOURCE LINES 79-82

.. code-block:: default


    from deepchecks.tabular.datasets.classification.phishing import load_data








.. GENERATED FROM PYTHON SOURCE LINES 83-86

.. code-block:: default


    df = load_data(data_format='dataframe', as_train_test=False)








.. GENERATED FROM PYTHON SOURCE LINES 87-90

.. code-block:: default


    df.shape





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (11350, 25)



.. GENERATED FROM PYTHON SOURCE LINES 91-94

.. code-block:: default


    df.head(5)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>target</th>
          <th>month</th>
          <th>scrape_date</th>
          <th>ext</th>
          <th>urlLength</th>
          <th>numDigits</th>
          <th>numParams</th>
          <th>num_%20</th>
          <th>num_@</th>
          <th>entropy</th>
          <th>has_ip</th>
          <th>hasHttp</th>
          <th>hasHttps</th>
          <th>urlIsLive</th>
          <th>dsr</th>
          <th>dse</th>
          <th>bodyLength</th>
          <th>numTitles</th>
          <th>numImages</th>
          <th>numLinks</th>
          <th>specialChars</th>
          <th>scriptLength</th>
          <th>sbr</th>
          <th>bscr</th>
          <th>sscr</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>1</td>
          <td>2019-01-01</td>
          <td>net</td>
          <td>102</td>
          <td>8</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>-4.384032</td>
          <td>0</td>
          <td>True</td>
          <td>False</td>
          <td>False</td>
          <td>4921</td>
          <td>191</td>
          <td>32486</td>
          <td>3</td>
          <td>5</td>
          <td>330</td>
          <td>9419</td>
          <td>23919</td>
          <td>0.736286</td>
          <td>0.289940</td>
          <td>2.539442</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0</td>
          <td>1</td>
          <td>2019-01-01</td>
          <td>country</td>
          <td>154</td>
          <td>60</td>
          <td>0</td>
          <td>2</td>
          <td>0</td>
          <td>-3.566515</td>
          <td>0</td>
          <td>True</td>
          <td>False</td>
          <td>False</td>
          <td>0</td>
          <td>0</td>
          <td>16199</td>
          <td>0</td>
          <td>4</td>
          <td>39</td>
          <td>2735</td>
          <td>794</td>
          <td>0.049015</td>
          <td>0.168838</td>
          <td>0.290311</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0</td>
          <td>1</td>
          <td>2019-01-01</td>
          <td>net</td>
          <td>171</td>
          <td>5</td>
          <td>11</td>
          <td>0</td>
          <td>0</td>
          <td>-4.608755</td>
          <td>0</td>
          <td>True</td>
          <td>False</td>
          <td>False</td>
          <td>5374</td>
          <td>104</td>
          <td>103344</td>
          <td>18</td>
          <td>9</td>
          <td>302</td>
          <td>27798</td>
          <td>83817</td>
          <td>0.811049</td>
          <td>0.268985</td>
          <td>2.412174</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0</td>
          <td>1</td>
          <td>2019-01-01</td>
          <td>com</td>
          <td>94</td>
          <td>10</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>-4.548921</td>
          <td>0</td>
          <td>True</td>
          <td>False</td>
          <td>False</td>
          <td>6107</td>
          <td>466</td>
          <td>34093</td>
          <td>11</td>
          <td>43</td>
          <td>199</td>
          <td>9087</td>
          <td>19427</td>
          <td>0.569824</td>
          <td>0.266536</td>
          <td>2.137889</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0</td>
          <td>1</td>
          <td>2019-01-01</td>
          <td>other</td>
          <td>95</td>
          <td>11</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>-4.717188</td>
          <td>0</td>
          <td>True</td>
          <td>False</td>
          <td>False</td>
          <td>3819</td>
          <td>928</td>
          <td>202</td>
          <td>1</td>
          <td>0</td>
          <td>0</td>
          <td>39</td>
          <td>0</td>
          <td>0.000000</td>
          <td>0.193069</td>
          <td>0.000000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 95-96

Here is the actual list of features:

.. GENERATED FROM PYTHON SOURCE LINES 96-99

.. code-block:: default


    df.columns





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Index(['target', 'month', 'scrape_date', 'ext', 'urlLength', 'numDigits',
           'numParams', 'num_%20', 'num_@', 'entropy', 'has_ip', 'hasHttp',
           'hasHttps', 'urlIsLive', 'dsr', 'dse', 'bodyLength', 'numTitles',
           'numImages', 'numLinks', 'specialChars', 'scriptLength', 'sbr', 'bscr',
           'sscr'],
          dtype='object')



.. GENERATED FROM PYTHON SOURCE LINES 100-133

Feature List
------------
And here is a short explanation of each:

=============  =========================   =======================================================================
Feature Name   Feature Group               Description 
=============  =========================   =======================================================================
target         Meta Features               0 if the URL is benign, 1 if it is related to phishing
month          Meta Features               The month this URL was first encountered, as an int
scrape_date    Meta Features               The exact date this URL was first encountered
ext            String Characteristics      The domain extension
urlLength      String Characteristics      The number of characters in the URL
numDigits      String Characteristics      The number of digits in the URL
numParams      String Characteristics      The number of query parameters in the URL
num_%20        String Characteristics      The number of '%20' substrings in the URL
num_@          String Characteristics      The number of @ characters in the URL
entropy        String Characteristics      The entropy of the URL
has_ip         String Characteristics      True if the URL string contains an IP addres
hasHttp        Domain Characteristics      True if the url's domain supports http
hasHttps       Domain Characteristics      True if the url's domain supports https
urlIsLive      Domain Characteristics      The URL was live at the time of scraping
dsr            Domain Characteristics      The number of days since domain registration
dse            Domain Characteristics      The number of days since domain registration expired
bodyLength     Web Page Characteristics    The number of charcters in the URL's web page
numTitles      Web Page Characteristics    The number of HTML titles (H1/H2/...) in the page
numImages      Web Page Characteristics    The number of images in the page
numLinks       Web Page Characteristics    The number of links in the page
specialChars   Web Page Characteristics    The number of special characters in the page
scriptLength   Web Page Characteristics    The number of charcters in scripts embedded in the page
sbr            Web Page Characteristics    The ratio of scriptLength to bodyLength (`= scriptLength / bodyLength`)
bscr           Web Page Characteristics    The ratio of bodyLength to specialChars (`= specialChars / bodyLength`)
sscr           Web Page Characteristics    The ratio of scriptLength to specialChars (`= scriptLength / specialChars`)
=============  =========================   =======================================================================

.. GENERATED FROM PYTHON SOURCE LINES 135-148

Data Integrity with Deepchecks!
===============================
The nice thing about the ``deepchecks`` package is that we can already use it out 
of the box! Instead of running a single check, we use a pre-defined test suite to
run a host of data validation checks.

We think it's valuable to start off with these types of suites as there are various
issues we can identify at the get go just by looking at raw data.

We will first import the appropriate factory function from the ``deepchecks.suites``
module - in this case, an integrity suite tailored for a single dataset (as
opposed to a division into a train and test, for example) - and use it to create a
new suite object:

.. GENERATED FROM PYTHON SOURCE LINES 148-153

.. code-block:: default


    from deepchecks.tabular.suites import data_integrity

    integ_suite = data_integrity()








.. GENERATED FROM PYTHON SOURCE LINES 154-160

We will now run that suite on our data. While running on the native DataFrame is possible in some cases, it is
recommended to wrap it with the ``deepchecks.tabular.Dataset`` object instead, to give
the package a bit more context, namely what is the label column, and whether
we have a datetime column (we have, as an index, so we'll set
``set_datetime_from_dataframe_index=True``), or any categorical features (we have
none after one-hot encoding them, so we'll set ``cat_features=[]`` explicitly).

.. GENERATED FROM PYTHON SOURCE LINES 160-165

.. code-block:: default


    dataset = deepchecks.tabular.Dataset(df=df, label='target',
                                         set_datetime_from_dataframe_index=True, cat_features=[])
    integ_suite.run(dataset)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Data Integrity Suite:
    |            | 0/12 [Time: 00:00]
    Data Integrity Suite:
    |███         | 3/12 [Time: 00:00, Check=Mixed Nulls]
    Data Integrity Suite:
    |██████      | 6/12 [Time: 00:00, Check=Data Duplicates]
    Data Integrity Suite:
    |████████    | 8/12 [Time: 00:01, Check=Conflicting Labels]
    Data Integrity Suite:
    |██████████  | 10/12 [Time: 00:07, Check=Feature Label Correlation]
    Data Integrity Suite:
    |████████████| 12/12 [Time: 00:07, Check=Identifier Label Correlation]
                                                                                            

.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <!--
        ~ ----------------------------------------------------------------------------
        ~ Copyright (C) 2021-2023 Deepchecks (https://www.deepchecks.com)
        ~
        ~ This file is part of Deepchecks.
        ~ Deepchecks is distributed under the terms of the GNU Affero General
        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
        ~ along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
        ~ ----------------------------------------------------------------------------
        ~
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"IPY_MODEL_b18496c65eb7447caab41c06cad253cb", "selected_index": null}}, "cd86f2b91802497db7d5db5ac33b7e76": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_be5798781f4045dfa09afbb11140b82c", "IPY_MODEL_7cd1d6962d034f08a396fcd04f55356a", "IPY_MODEL_4997e274c2774e9c9155d55ed486d1a2", "IPY_MODEL_ab018010e7534b3e8d354a8ab75be85a", "IPY_MODEL_93d99060d283479c9363ee5bfaa70bc9"], "layout": "IPY_MODEL_7c36a795af1e42e7a43395a95e5d65b5"}}, "be5798781f4045dfa09afbb11140b82c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bbe62ee1ce31469ca75c48075c1293fc", "style": "IPY_MODEL_3db1ea7a0593471496ae079acd12a7d0", "value": "<style type=\"text/css\">\n#T_79b87_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79b87_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79b87_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79b87_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79b87_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_79b87_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_79b87_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_79b87_row0_col1\" class=\"data row0 col1\" ><a href=\"#SingleValueinColumn_8RU75B8HZJHLPGDGVH5C4IU6E-section-GP6IR\">Single Value in Column</a></td>\n      <td id=\"T_79b87_row0_col2\" class=\"data row0 col2\" >Does not contain only a single value</td>\n      <td id=\"T_79b87_row0_col3\" class=\"data row0 col3\" >Found 2 out of 25 columns with a single value: ['has_ip', 'urlIsLive']</td>\n    </tr>\n    <tr>\n      <td id=\"T_79b87_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_79b87_row1_col1\" class=\"data row1 col1\" ><a href=\"#Feature-FeatureCorrelation_8RU75B8HZJHLPGDGVH5C4IU6E-section-GP6IR\">Feature-Feature Correlation</a></td>\n      <td id=\"T_79b87_row1_col2\" class=\"data row1 col2\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_79b87_row1_col3\" class=\"data row1 col3\" >Correlation is greater than 0.9 for pairs [('bodyLength', 'specialChars'), ('bodyLength', 'scriptLength'), ('scriptLength', 'specialChars'), ('sbr', 'sscr')]</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bbe62ee1ce31469ca75c48075c1293fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3db1ea7a0593471496ae079acd12a7d0": {"model_name": 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"jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_08baf3b15ed8410da0a4327ffe892838", "IPY_MODEL_4aa8eb93409542ce9deb80be02bc32a2", "IPY_MODEL_3b545f8f9df84e069008b548b05827d8", "IPY_MODEL_215e63ca3f844523ac4f98a36eeb7dfe"], "layout": "IPY_MODEL_5b000ce2043d43b4a75e92653ba32553"}}, "08baf3b15ed8410da0a4327ffe892838": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ab27933a8dd24eda9a6e347ffb6aae31", "style": "IPY_MODEL_2050450626aa4afb8c0c20c202152ec0", "value": "<h4 id=\"SingleValueinColumn_8RU75B8HZJHLPGDGVH5C4IU6E-section-GP6IR\"><b>Single Value in Column</b></h4>"}}, "ab27933a8dd24eda9a6e347ffb6aae31": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2050450626aa4afb8c0c20c202152ec0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4aa8eb93409542ce9deb80be02bc32a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_12d554d3192a4b5f95a70a5af6bb8284", "style": "IPY_MODEL_ea28721b121b4167b551d047a2cad38a", "value": "<p>Check if there are columns which have only a single unique value in all rows. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/data_integrity/plot_is_single_value.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "12d554d3192a4b5f95a70a5af6bb8284": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea28721b121b4167b551d047a2cad38a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b545f8f9df84e069008b548b05827d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c3d6d8f486649a49ad1314e94d4315f", "style": "IPY_MODEL_3205376682b04610891b62c310b5f857", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c5187_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5187_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5187_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5187_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5187_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c5187_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c5187_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_c5187_row0_col1\" class=\"data row0 col1\" >Does not contain only a single value</td>\n      <td id=\"T_c5187_row0_col2\" class=\"data row0 col2\" >Found 2 out of 25 columns with a single value: ['has_ip', 'urlIsLive']</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1c3d6d8f486649a49ad1314e94d4315f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3205376682b04610891b62c310b5f857": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "215e63ca3f844523ac4f98a36eeb7dfe": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_11220013d8f847f1995da906c5ab4509", "IPY_MODEL_64aa7e9a19ab47c88b5789720e6272b7", "IPY_MODEL_e62e5a134bca438e998cdedf44b423fb", "IPY_MODEL_1488af0a600b46328d1b6683dd383795"], "layout": "IPY_MODEL_c1ca00f99e1f481ea8fc95befef73c8b"}}, "11220013d8f847f1995da906c5ab4509": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48fd8a01ac8048f28d5872f906720beb", "style": "IPY_MODEL_da245db916124da9983507b52336e817", "value": "<h5><b>Additional Outputs</b></h5>"}}, "48fd8a01ac8048f28d5872f906720beb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da245db916124da9983507b52336e817": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "64aa7e9a19ab47c88b5789720e6272b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3530f5a5efb4c5aafa2e83465b63d6f", "style": "IPY_MODEL_ae5556458f954ef6ba4d06117941f65f", "value": "<div>The following columns have only one unique value</div>"}}, "c3530f5a5efb4c5aafa2e83465b63d6f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae5556458f954ef6ba4d06117941f65f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e62e5a134bca438e998cdedf44b423fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c03820b4b9194c6a8df5c5705da27862", "style": "IPY_MODEL_364e4259962a4d80b7ef189f2a1fcfe1", "value": "<style type=\"text/css\">\n#T_d68ac_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d68ac_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d68ac_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d68ac_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d68ac_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d68ac_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >has_ip</th>\n      <th class=\"col_heading level0 col1\" >urlIsLive</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_d68ac_level0_row0\" class=\"row_heading level0 row0\" >Single unique value</th>\n      <td id=\"T_d68ac_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_d68ac_row0_col1\" class=\"data row0 col1\" >False</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c03820b4b9194c6a8df5c5705da27862": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "364e4259962a4d80b7ef189f2a1fcfe1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1488af0a600b46328d1b6683dd383795": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0d67eb6cc2894a93927776a08e3ab2e0", "style": "IPY_MODEL_99042718e65047359e8b46ff661d0897", "value": "<br><a href=\"#summary_8RU75B8HZJHLPGDGVH5C4IU6E-section-GP6IR\" style=\"font-size: 14px;\">Go to top</a>"}}, "0d67eb6cc2894a93927776a08e3ab2e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "99042718e65047359e8b46ff661d0897": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c1ca00f99e1f481ea8fc95befef73c8b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5b000ce2043d43b4a75e92653ba32553": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab018010e7534b3e8d354a8ab75be85a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c1465bd2bfa74b458aa1b1f3a8aedd99", "style": "IPY_MODEL_0e4674a0bcf4411f8c28b376892e740f", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c1465bd2bfa74b458aa1b1f3a8aedd99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e4674a0bcf4411f8c28b376892e740f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93d99060d283479c9363ee5bfaa70bc9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_d436a5bfef764ce1a9794cbb8e8c2124", "IPY_MODEL_cf5b5d9a17004fcf9ef5efc95c67f584", "IPY_MODEL_f0683ea9ac7e404f937ff3407fa3ba6b", "IPY_MODEL_3e5065948c834ebaa13abfd12972320c"], "layout": "IPY_MODEL_c35a5e223971423a9cff6fe54fd29ed4"}}, "d436a5bfef764ce1a9794cbb8e8c2124": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a51ae486bf2c460da3a7a3018e2bff62", "style": "IPY_MODEL_b7ab0bbf7acb48ea8b15f0116675f9a3", "value": "<h4 id=\"Feature-FeatureCorrelation_8RU75B8HZJHLPGDGVH5C4IU6E-section-GP6IR\"><b>Feature-Feature Correlation</b></h4>"}}, "a51ae486bf2c460da3a7a3018e2bff62": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7ab0bbf7acb48ea8b15f0116675f9a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cf5b5d9a17004fcf9ef5efc95c67f584": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_25c7c91eb35542c08af4820d90ff452c", "style": "IPY_MODEL_3ac191b91ad44b69aae9efa7b24b8eb2", "value": "<p>    Checks for pairwise correlation between the features. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/data_integrity/plot_feature_feature_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "25c7c91eb35542c08af4820d90ff452c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ac191b91ad44b69aae9efa7b24b8eb2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f0683ea9ac7e404f937ff3407fa3ba6b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9bbda7aeb6c84b84a310cc1bda8de61e", "style": "IPY_MODEL_3c3a302fc07141368df0e0b5e6c27ba3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c4356_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c4356_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c4356_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c4356_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c4356_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c4356_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c4356_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_c4356_row0_col1\" class=\"data row0 col1\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_c4356_row0_col2\" class=\"data row0 col2\" >Correlation is greater than 0.9 for pairs [('bodyLength', 'specialChars'), ('bodyLength', 'scriptLength'), ('scriptLength', 'specialChars'), ('sbr', 'sscr')]</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9bbda7aeb6c84b84a310cc1bda8de61e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c3a302fc07141368df0e0b5e6c27ba3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e5065948c834ebaa13abfd12972320c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c6b94a8f714b4d78895552efffab70be", "IPY_MODEL_736009e29ffb46678b6e6d305c4d225a", "IPY_MODEL_4ea1021dcdd0489cb89b038beafe3dae", "IPY_MODEL_08600b74103644df8b323a9f0943e61b", "IPY_MODEL_d1902b68af664e8499dad1bd98195996", "IPY_MODEL_7e825bb5eee84da1ad478cadf0c437ea"], "layout": "IPY_MODEL_5098ccd4a36b42bfb8d0d9251b9291d1"}}, "c6b94a8f714b4d78895552efffab70be": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_abdc04d30bd14178a1d5af936a284ce6", "style": "IPY_MODEL_4b8e07d7de584370ae58fd15eb69df34", "value": "<h5><b>Additional Outputs</b></h5>"}}, "abdc04d30bd14178a1d5af936a284ce6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b8e07d7de584370ae58fd15eb69df34": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "736009e29ffb46678b6e6d305c4d225a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hovertemplate": "x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>", "name": "0", "x": ["month", "urlLength", "numDigits", "numParams", "num_%20", "num_@", "entropy", "hasHttp", "hasHttps", "dsr"], "xaxis": "x", "y": ["month", "urlLength", "numDigits", "numParams", "num_%20", "num_@", "entropy", "hasHttp", "hasHttps", "dsr"], "yaxis": "y", "z": [[1.0, 0.01015727369094894, 0.026103451780963686, 0.10320726214326129, 0.022097641590444313, 0.0018847378024763445, 6.118727514641124e-05, 0.006463954060400642, 0.0046284769829413415, 0.004131686098725467], [0.01015727369094894, 1.0, 0.3722786911668201, 0.16492194702158086, 0.05839153495095739, 0.01930329769289281, 0.1395074579944866, 0.010267232762478502, 0.011461965286746542, 0.07454063348762527], [0.026103451780963686, 0.3722786911668201, 1.0, 0.007415529506369248, 0.12726910636113095, 0.007018899247945417, 0.32985718309074363, 0.022735323948234666, 0.029221845549561604, 0.12340078004833055], [0.10320726214326129, 0.16492194702158086, 0.007415529506369248, 1.0, 0.026501669786870914, 0.03422000295482532, 0.2936585078100171, 0.038101529366260184, 0.045638861322073336, 0.03200069391186678], [0.022097641590444313, 0.05839153495095739, 0.12726910636113095, 0.026501669786870914, 1.0, 0.008835962975163045, 0.12342697368377037, 0.017981991923180944, 0.018417317547710095, 0.072815128068547], [0.0018847378024763445, 0.01930329769289281, 0.007018899247945417, 0.03422000295482532, 0.008835962975163045, 1.0, 0.050896111458707864, 0.25250358116679794, 0.24965047778929247, 0.0655493980096069], [6.118727514641124e-05, 0.1395074579944866, 0.32985718309074363, 0.2936585078100171, 0.12342697368377037, 0.050896111458707864, 1.0, 0.06653107447355738, 0.062362662374954456, 0.03539342389339207], [0.006463954060400642, 0.010267232762478502, 0.022735323948234666, 0.038101529366260184, 0.017981991923180944, 0.25250358116679794, 0.06653107447355738, 1.0, 0.9889345255951079, 0.1408626650261827], [0.0046284769829413415, 0.011461965286746542, 0.029221845549561604, 0.045638861322073336, 0.018417317547710095, 0.24965047778929247, 0.062362662374954456, 0.9889345255951079, 1.0, 0.14142376863212394], [0.004131686098725467, 0.07454063348762527, 0.12340078004833055, 0.03200069391186678, 0.072815128068547, 0.0655493980096069, 0.03539342389339207, 0.1408626650261827, 0.14142376863212394, 1.0]], "type": "heatmap", "uid": "97c77258-c9e4-4c45-81b5-e4f5d9225097"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"coloraxis": {"colorscale": [[0.0, "rgb(3, 35, 51)"], [0.09090909090909091, "rgb(13, 48, 100)"], [0.18181818181818182, "rgb(53, 50, 155)"], [0.2727272727272727, "rgb(93, 62, 153)"], [0.36363636363636365, "rgb(126, 77, 143)"], [0.45454545454545453, "rgb(158, 89, 135)"], [0.5454545454545454, "rgb(193, 100, 121)"], [0.6363636363636364, "rgb(225, 113, 97)"], [0.7272727272727273, "rgb(246, 139, 69)"], [0.8181818181818182, "rgb(251, 173, 60)"], [0.9090909090909091, "rgb(246, 211, 70)"], [1.0, "rgb(231, 250, 90)"]]}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y"}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0]}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "4ea1021dcdd0489cb89b038beafe3dae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b6fe6c4fa424d72ad8c5c075cc59faa", "style": "IPY_MODEL_a82b3fa0e57e4a76b8fc208d11bb3d8c", "value": "<div>* Displayed as absolute values.</div>"}}, "9b6fe6c4fa424d72ad8c5c075cc59faa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a82b3fa0e57e4a76b8fc208d11bb3d8c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08600b74103644df8b323a9f0943e61b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5b1343eb93d244a9be31d2dd2bf5527d", "style": "IPY_MODEL_37bc77874f444cf38dd79ffd2d690a07", "value": "<div>* Some features in the dataset are neither numerical nor categorical and therefore not calculated.</div>"}}, "5b1343eb93d244a9be31d2dd2bf5527d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37bc77874f444cf38dd79ffd2d690a07": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d1902b68af664e8499dad1bd98195996": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d7fe5a224f3d41d8b9049970b9701db7", "style": "IPY_MODEL_ff9d4b073e62459e8381d6da3eeba8b5", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 10000 samples out of 11350. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "d7fe5a224f3d41d8b9049970b9701db7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ff9d4b073e62459e8381d6da3eeba8b5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7e825bb5eee84da1ad478cadf0c437ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd7379b395b74891bb04142166759c8c", "style": "IPY_MODEL_6e5178c2eda74e27a0180a4293f37e3b", "value": "<br><a href=\"#summary_8RU75B8HZJHLPGDGVH5C4IU6E-section-GP6IR\" style=\"font-size: 14px;\">Go to top</a>"}}, "fd7379b395b74891bb04142166759c8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e5178c2eda74e27a0180a4293f37e3b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5098ccd4a36b42bfb8d0d9251b9291d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c35a5e223971423a9cff6fe54fd29ed4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7c36a795af1e42e7a43395a95e5d65b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b18496c65eb7447caab41c06cad253cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3599ad1232314170b3d9b5708c2865e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "41c6e3568c0a4ffd879506746edc5f1a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_85aacfad0aad4e7299c56bd52a0071dd", "IPY_MODEL_59a342205d8d449badde56a3f8db1b5a"], "layout": "IPY_MODEL_4db7f7e471c245418ea0deecf95b9c02"}}, "85aacfad0aad4e7299c56bd52a0071dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7aec9b1df89a491fbac38d36c8fd4a06", "style": "IPY_MODEL_d5514eb8ed964cc4aa1e7ed0698cf902", "value": "<span id=\"summary_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\"></span>"}}, "7aec9b1df89a491fbac38d36c8fd4a06": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5514eb8ed964cc4aa1e7ed0698cf902": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59a342205d8d449badde56a3f8db1b5a": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_d1a24afdb5e846abb8515234e5af2204"], "layout": "IPY_MODEL_b8c3b98215fb48e5bdebb4bf463331f8", "selected_index": null}}, "d1a24afdb5e846abb8515234e5af2204": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_acf1c59ce2b144f58e77952750097a53", "IPY_MODEL_c1d47fb76cd1446286f09c31de8eca1e", "IPY_MODEL_30980dad85784cc8a7336363f4cadc9d", "IPY_MODEL_337b6a483d8448588995044e54bc6842", "IPY_MODEL_17b6d63aad6844cbb8ec7bfd3d6f2d95"], "layout": "IPY_MODEL_337c4fdc4cd84d7a84532b84dc9285d4"}}, "acf1c59ce2b144f58e77952750097a53": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b34bc75983984236b02e58df00d0b031", "style": "IPY_MODEL_9454d5d497d84e019c5d81b82176f352", "value": "<style type=\"text/css\">\n#T_60283_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60283_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60283_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60283_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60283_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_60283_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_60283_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row0_col1\" class=\"data row0 col1\" ><a href=\"#FeatureLabelCorrelation_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\">Feature Label Correlation</a></td>\n      <td id=\"T_60283_row0_col2\" class=\"data row0 col2\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_60283_row0_col3\" class=\"data row0 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row1_col1\" class=\"data row1 col1\" ><a href=\"#DataDuplicates_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\">Data Duplicates</a></td>\n      <td id=\"T_60283_row1_col2\" class=\"data row1 col2\" >Duplicate data ratio is less or equal to 5%</td>\n      <td id=\"T_60283_row1_col3\" class=\"data row1 col3\" >Found 0.0088% duplicate data</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row2_col1\" class=\"data row2 col1\" >Special Characters</td>\n      <td id=\"T_60283_row2_col2\" class=\"data row2 col2\" >Ratio of samples containing solely special character is less or equal to 0.1%</td>\n      <td id=\"T_60283_row2_col3\" class=\"data row2 col3\" >Passed for 25 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row3_col1\" class=\"data row3 col1\" >Mixed Nulls</td>\n      <td id=\"T_60283_row3_col2\" class=\"data row3 col2\" >Number of different null types is less or equal to 1</td>\n      <td id=\"T_60283_row3_col3\" class=\"data row3 col3\" >Passed for 25 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row4_col1\" class=\"data row4 col1\" >String Mismatch</td>\n      <td id=\"T_60283_row4_col2\" class=\"data row4 col2\" >No string variants</td>\n      <td id=\"T_60283_row4_col3\" class=\"data row4 col3\" >Passed for 1 relevant column</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row5_col1\" class=\"data row5 col1\" >Mixed Data Types</td>\n      <td id=\"T_60283_row5_col2\" class=\"data row5 col2\" >Rare data types in column are either more than 10% or less than 1% of the data</td>\n      <td id=\"T_60283_row5_col3\" class=\"data row5 col3\" >25 columns passed: found 0 columns with negligible types mix, and 25 columns without any types mix</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row6_col1\" class=\"data row6 col1\" >String Length Out Of Bounds</td>\n      <td id=\"T_60283_row6_col2\" class=\"data row6 col2\" >Ratio of string length outliers is less or equal to 0%</td>\n      <td id=\"T_60283_row6_col3\" class=\"data row6 col3\" >Passed for 2 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row7_col1\" class=\"data row7 col1\" >Conflicting Labels</td>\n      <td id=\"T_60283_row7_col2\" class=\"data row7 col2\" >Ambiguous sample ratio is less or equal to 0%</td>\n      <td id=\"T_60283_row7_col3\" class=\"data row7 col3\" >Ratio of samples with conflicting labels: 0%</td>\n    </tr>\n    <tr>\n      <td id=\"T_60283_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_60283_row8_col1\" class=\"data row8 col1\" >Identifier Label Correlation</td>\n      <td id=\"T_60283_row8_col2\" class=\"data row8 col2\" >Identifier columns PPS is less or equal to 0</td>\n      <td id=\"T_60283_row8_col3\" class=\"data row8 col3\" >Passed for 1 relevant column</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b34bc75983984236b02e58df00d0b031": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9454d5d497d84e019c5d81b82176f352": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c1d47fb76cd1446286f09c31de8eca1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87a20eeec11a474f833f0f61075ec325", "style": "IPY_MODEL_13aa1f31a3504eed81a06fb2d387e3e7", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "87a20eeec11a474f833f0f61075ec325": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13aa1f31a3504eed81a06fb2d387e3e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "30980dad85784cc8a7336363f4cadc9d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_bdd702907618418aa614588a60cb9d6e", "IPY_MODEL_28776ad75ad14072b2d0ea92377d7328", "IPY_MODEL_8598aced2c1c4a339a2a2c9e4fec9062", "IPY_MODEL_d160e242b2c54db6a559172207bdc7e8"], "layout": "IPY_MODEL_137bb98441ae4ed997f2ba4f411b49ed"}}, "bdd702907618418aa614588a60cb9d6e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_613f9c748436479bac719ee7c83412f0", "style": "IPY_MODEL_0fa59bc7bf9948f8ad9470968bb70eb6", "value": "<h4 id=\"DataDuplicates_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\"><b>Data Duplicates</b></h4>"}}, "613f9c748436479bac719ee7c83412f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0fa59bc7bf9948f8ad9470968bb70eb6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "28776ad75ad14072b2d0ea92377d7328": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_91e34dbe0f9945d891331a2cef00f064", "style": "IPY_MODEL_c24bd9ab812843b28396330590c15ba3", "value": "<p>Checks for duplicate samples in the dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/data_integrity/plot_data_duplicates.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "91e34dbe0f9945d891331a2cef00f064": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c24bd9ab812843b28396330590c15ba3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8598aced2c1c4a339a2a2c9e4fec9062": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11e9d155be034830ab646e80addae4f3", "style": "IPY_MODEL_cf79e6d81bbf4ef581086a3b661085c2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_eeb87_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eeb87_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eeb87_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eeb87_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_eeb87_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_eeb87_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_eeb87_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_eeb87_row0_col1\" class=\"data row0 col1\" >Duplicate data ratio is less or equal to 5%</td>\n      <td id=\"T_eeb87_row0_col2\" class=\"data row0 col2\" >Found 0.0088% duplicate data</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "11e9d155be034830ab646e80addae4f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf79e6d81bbf4ef581086a3b661085c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d160e242b2c54db6a559172207bdc7e8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ed50dc36a70e43e2b03f209d3fff14d7", "IPY_MODEL_a7a03cdcce5a412895b8f98ffeb2d8ba", "IPY_MODEL_c4b36f06716b43fdb7a0435fbed6a3fe", "IPY_MODEL_173cbb45017545229d5d16c2bf49e4e0", "IPY_MODEL_742c39cb9126461981b1204646da229b"], "layout": "IPY_MODEL_cfef803f9b4b45d7bad26503ccb30a0a"}}, "ed50dc36a70e43e2b03f209d3fff14d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7ddc094d05ea4f2b8f74b1bfa9636131", "style": "IPY_MODEL_0a21cf2d02c94fc98cf2b2153e8b96a2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7ddc094d05ea4f2b8f74b1bfa9636131": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a21cf2d02c94fc98cf2b2153e8b96a2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7a03cdcce5a412895b8f98ffeb2d8ba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a476920ab3842bdb58923940daee7e7", "style": "IPY_MODEL_8871200477a84b058f0ca4fe6eb5bad9", "value": "<div>0.0088% of data samples are duplicates. </div>"}}, "8a476920ab3842bdb58923940daee7e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8871200477a84b058f0ca4fe6eb5bad9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c4b36f06716b43fdb7a0435fbed6a3fe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6ebcd0c85b7f438d9101d6fb49b93f02", "style": "IPY_MODEL_c46e72f1ae804ad9a777300dfd80df40", "value": "<div>Each row in the table shows an example of duplicate data and the number of times it appears.</div>"}}, "6ebcd0c85b7f438d9101d6fb49b93f02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c46e72f1ae804ad9a777300dfd80df40": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "173cbb45017545229d5d16c2bf49e4e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c3647d661094192aa03f288657a470b", "style": "IPY_MODEL_3111c7b3c6b94b70acefdff0be7fdc39", "value": "<style type=\"text/css\">\n#T_4a750_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4a750_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4a750_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4a750_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4a750_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_4a750_\">\n  <thead>\n    <tr>\n      <th class=\"blank\" >&nbsp;</th>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >target</th>\n      <th class=\"col_heading level0 col1\" >month</th>\n      <th class=\"col_heading level0 col2\" >scrape_date</th>\n      <th class=\"col_heading level0 col3\" >ext</th>\n      <th class=\"col_heading level0 col4\" >urlLength</th>\n      <th class=\"col_heading level0 col5\" >numDigits</th>\n      <th class=\"col_heading level0 col6\" >numParams</th>\n      <th class=\"col_heading level0 col7\" >num_%20</th>\n      <th class=\"col_heading level0 col8\" >num_@</th>\n      <th class=\"col_heading level0 col9\" >entropy</th>\n      <th class=\"col_heading level0 col10\" >has_ip</th>\n      <th class=\"col_heading level0 col11\" >hasHttp</th>\n      <th class=\"col_heading level0 col12\" >hasHttps</th>\n      <th class=\"col_heading level0 col13\" >urlIsLive</th>\n      <th class=\"col_heading level0 col14\" >dsr</th>\n      <th class=\"col_heading level0 col15\" >dse</th>\n      <th class=\"col_heading level0 col16\" >bodyLength</th>\n      <th class=\"col_heading level0 col17\" >numTitles</th>\n      <th class=\"col_heading level0 col18\" >numImages</th>\n      <th class=\"col_heading level0 col19\" >numLinks</th>\n      <th class=\"col_heading level0 col20\" >specialChars</th>\n      <th class=\"col_heading level0 col21\" >scriptLength</th>\n      <th class=\"col_heading level0 col22\" >sbr</th>\n      <th class=\"col_heading level0 col23\" >bscr</th>\n      <th class=\"col_heading level0 col24\" >sscr</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Instances</th>\n      <th class=\"index_name level1\" >Number of Duplicates</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n      <th class=\"blank col10\" >&nbsp;</th>\n      <th class=\"blank col11\" >&nbsp;</th>\n      <th class=\"blank col12\" >&nbsp;</th>\n      <th class=\"blank col13\" >&nbsp;</th>\n      <th class=\"blank col14\" >&nbsp;</th>\n      <th class=\"blank col15\" >&nbsp;</th>\n      <th class=\"blank col16\" >&nbsp;</th>\n      <th class=\"blank col17\" >&nbsp;</th>\n      <th class=\"blank col18\" >&nbsp;</th>\n      <th class=\"blank col19\" >&nbsp;</th>\n      <th class=\"blank col20\" >&nbsp;</th>\n      <th class=\"blank col21\" >&nbsp;</th>\n      <th class=\"blank col22\" >&nbsp;</th>\n      <th class=\"blank col23\" >&nbsp;</th>\n      <th class=\"blank col24\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_4a750_level0_row0\" class=\"row_heading level0 row0\" >4696, 4719</th>\n      <th id=\"T_4a750_level1_row0\" class=\"row_heading level1 row0\" >2</th>\n      <td id=\"T_4a750_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_4a750_row0_col1\" class=\"data row0 col1\" >6</td>\n      <td id=\"T_4a750_row0_col2\" class=\"data row0 col2\" >2019-06-06</td>\n      <td id=\"T_4a750_row0_col3\" class=\"data row0 col3\" >other</td>\n      <td id=\"T_4a750_row0_col4\" class=\"data row0 col4\" >123</td>\n      <td id=\"T_4a750_row0_col5\" class=\"data row0 col5\" >28</td>\n      <td id=\"T_4a750_row0_col6\" class=\"data row0 col6\" >4</td>\n      <td id=\"T_4a750_row0_col7\" class=\"data row0 col7\" >0</td>\n      <td id=\"T_4a750_row0_col8\" class=\"data row0 col8\" >0</td>\n      <td id=\"T_4a750_row0_col9\" class=\"data row0 col9\" >-4.91</td>\n      <td id=\"T_4a750_row0_col10\" class=\"data row0 col10\" >0</td>\n      <td id=\"T_4a750_row0_col11\" class=\"data row0 col11\" >True</td>\n      <td id=\"T_4a750_row0_col12\" class=\"data row0 col12\" >False</td>\n      <td id=\"T_4a750_row0_col13\" class=\"data row0 col13\" >False</td>\n      <td id=\"T_4a750_row0_col14\" class=\"data row0 col14\" >0</td>\n      <td id=\"T_4a750_row0_col15\" class=\"data row0 col15\" >0</td>\n      <td id=\"T_4a750_row0_col16\" class=\"data row0 col16\" >0</td>\n      <td id=\"T_4a750_row0_col17\" class=\"data row0 col17\" >0</td>\n      <td id=\"T_4a750_row0_col18\" class=\"data row0 col18\" >0</td>\n      <td id=\"T_4a750_row0_col19\" class=\"data row0 col19\" >0</td>\n      <td id=\"T_4a750_row0_col20\" class=\"data row0 col20\" >0</td>\n      <td id=\"T_4a750_row0_col21\" class=\"data row0 col21\" >0</td>\n      <td id=\"T_4a750_row0_col22\" class=\"data row0 col22\" >0.00</td>\n      <td id=\"T_4a750_row0_col23\" class=\"data row0 col23\" >0.00</td>\n      <td id=\"T_4a750_row0_col24\" class=\"data row0 col24\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7c3647d661094192aa03f288657a470b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3111c7b3c6b94b70acefdff0be7fdc39": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "742c39cb9126461981b1204646da229b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e4b35f79a2164ce28d1618bb28974621", "style": "IPY_MODEL_f274b7bb45b1456b8afdc0058f1dbde1", "value": "<br><a href=\"#summary_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\" style=\"font-size: 14px;\">Go to top</a>"}}, "e4b35f79a2164ce28d1618bb28974621": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f274b7bb45b1456b8afdc0058f1dbde1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cfef803f9b4b45d7bad26503ccb30a0a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "137bb98441ae4ed997f2ba4f411b49ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "337b6a483d8448588995044e54bc6842": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_efca610185af41de94dbf6cc51ca6c33", "style": "IPY_MODEL_e563a1b3366d48a68720c8cad44c6203", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "efca610185af41de94dbf6cc51ca6c33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e563a1b3366d48a68720c8cad44c6203": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17b6d63aad6844cbb8ec7bfd3d6f2d95": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_8f2991141c114883a51faf0dd7a71f0f", "IPY_MODEL_255a3dde994d4a2d9403fc5bd7c7f71d", "IPY_MODEL_8e070eb70a96487e9d970cf495a6d78d", "IPY_MODEL_3cd220e07fe54dde81a576c96de81b48"], "layout": "IPY_MODEL_8bb290d726f34ce0a237664e04a6c890"}}, "8f2991141c114883a51faf0dd7a71f0f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_61b1939aedc544fdbbfb72fea4ee5238", "style": "IPY_MODEL_3bf1640fd16e45f6ae410396cecb8caa", "value": "<h4 id=\"FeatureLabelCorrelation_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\"><b>Feature Label Correlation</b></h4>"}}, "61b1939aedc544fdbbfb72fea4ee5238": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bf1640fd16e45f6ae410396cecb8caa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "255a3dde994d4a2d9403fc5bd7c7f71d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_34077d6db8474632834b8b1d92d4733b", "style": "IPY_MODEL_b6e9956a5a5e49f1a56e25ae7c3babe6", "value": "<p>Return the PPS (Predictive Power Score) of all features in relation to the label. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/data_integrity/plot_feature_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "34077d6db8474632834b8b1d92d4733b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6e9956a5a5e49f1a56e25ae7c3babe6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e070eb70a96487e9d970cf495a6d78d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ee0d49e961054ce697b6b5d477b9c60a", "style": "IPY_MODEL_1f695fa32b7844b7a30a6c47d3044afe", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c9321_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c9321_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c9321_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c9321_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c9321_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c9321_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c9321_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c9321_row0_col1\" class=\"data row0 col1\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_c9321_row0_col2\" class=\"data row0 col2\" >Passed for 24 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ee0d49e961054ce697b6b5d477b9c60a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f695fa32b7844b7a30a6c47d3044afe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3cd220e07fe54dde81a576c96de81b48": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b5c711a2a5944f2298dd8bbea05c640d", "IPY_MODEL_3a8a0c2e82cf40ccb21ce80567fb2d62", "IPY_MODEL_01442f6facfc4f1ca5e43a7f5670414e", "IPY_MODEL_dcaacaf45fce41e084e5a8e4135a0f8c"], "layout": "IPY_MODEL_da53fa74c5f64a749758e0078f0d345f"}}, "b5c711a2a5944f2298dd8bbea05c640d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_244fd292abd344d0a5bc2c5f4070509d", "style": "IPY_MODEL_71b24527b9414517884274679460c445", "value": "<h5><b>Additional Outputs</b></h5>"}}, "244fd292abd344d0a5bc2c5f4070509d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71b24527b9414517884274679460c445": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a8a0c2e82cf40ccb21ce80567fb2d62": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.71</b>", "<b>0.27</b>", "<b>0.23</b>", "<b>0.12</b>", "<b>0.07</b>"], "textposition": "outside", "x": ["urlLength", "dsr", "dse", "specialChars", "bscr"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "0843caca-097b-4949-acca-76866a0c3035"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": 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"autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 7], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "RmNMrZ6z5j+JMPEdyP3QP9NLJR39Rs0/O3h7KBHMvj9yXSZr8iOxPw=="}]}, "01442f6facfc4f1ca5e43a7f5670414e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_361d0b2c7b3f43178a6bd6606da5717f", "style": "IPY_MODEL_3485dc3d25814495a89960d04347d6ea", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself (Read more about <a href=https://docs.deepchecks.com/stable/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>). A high PPS (close to 1) can mean that this feature's success in predicting the label is actually due to data leakage - meaning that the feature holds information that is based on the label to begin with.</div>"}}, "361d0b2c7b3f43178a6bd6606da5717f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3485dc3d25814495a89960d04347d6ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dcaacaf45fce41e084e5a8e4135a0f8c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95ba26a59b1949c2ab9ff85c171ff776", "style": "IPY_MODEL_894425545583400f801b861bc8290c4e", "value": "<br><a href=\"#summary_8RU75B8HZJHLPGDGVH5C4IU6E-section-CIHY5\" style=\"font-size: 14px;\">Go to top</a>"}}, "95ba26a59b1949c2ab9ff85c171ff776": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "894425545583400f801b861bc8290c4e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da53fa74c5f64a749758e0078f0d345f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8bb290d726f34ce0a237664e04a6c890": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "337c4fdc4cd84d7a84532b84dc9285d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b8c3b98215fb48e5bdebb4bf463331f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4db7f7e471c245418ea0deecf95b9c02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36754f3a0ef1452fad969bd322e81b41": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_31e25ec878324d8c81dee4bd30a41523", "IPY_MODEL_7a635ffc2c6146ae82b3501544db6ffb"], "layout": "IPY_MODEL_acd55f8cc6a74fe7957c2be6c100ff7e"}}, "31e25ec878324d8c81dee4bd30a41523": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_76de3a0a17d848b6a6584601e647b1b4", "style": "IPY_MODEL_78c886b56d194f0ead58079da206d3d3", "value": "<span id=\"summary_8RU75B8HZJHLPGDGVH5C4IU6E-section-CAUD6\"></span>"}}, "76de3a0a17d848b6a6584601e647b1b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "78c886b56d194f0ead58079da206d3d3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a635ffc2c6146ae82b3501544db6ffb": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_a4cf33269deb4ecdb8c1456981658aeb"], "layout": "IPY_MODEL_8a703b21b34547df8dafa4be29a2c433", "selected_index": null}}, "a4cf33269deb4ecdb8c1456981658aeb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d31aff89a8da4cd79f1117559640f957", "IPY_MODEL_7d805be75642452b8f2c7ec65e99d204", "IPY_MODEL_05179ff8d9254428ba8f94f44f0a9621"], "layout": "IPY_MODEL_57e684919ce942a39fb47b3714cf1e44"}}, "d31aff89a8da4cd79f1117559640f957": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cd477a27cce24991a6b741aaa6179349", "style": "IPY_MODEL_630706e9e5e34c69a8bd3d891e67aa60", "value": "<style type=\"text/css\">\n#T_51b25_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51b25_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51b25_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51b25_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51b25_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_51b25_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_51b25_row0_col0\" class=\"data row0 col0\" ><a href=\"#OutlierSampleDetection_8RU75B8HZJHLPGDGVH5C4IU6E-section-CAUD6\">Outlier Sample Detection</a></td>\n      <td id=\"T_51b25_row0_col1\" class=\"data row0 col1\" >Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/data_integrity/plot_outlier_sample_detection.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "cd477a27cce24991a6b741aaa6179349": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "630706e9e5e34c69a8bd3d891e67aa60": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d805be75642452b8f2c7ec65e99d204": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a66b0bb977e54c7eb8239069b0d36c57", "style": "IPY_MODEL_37a37e6deecc46149adc745403235459", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a66b0bb977e54c7eb8239069b0d36c57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37a37e6deecc46149adc745403235459": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "05179ff8d9254428ba8f94f44f0a9621": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_ab95240651154f99af695e19e39ffe0f", "IPY_MODEL_efc9dd951fef485d889ecc217078648a", "IPY_MODEL_b90a5b75cd004581bc133013d56ff7d8"], "layout": "IPY_MODEL_a2c1162e273348d39e12bd86c6c1cbc3"}}, "ab95240651154f99af695e19e39ffe0f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c588be340e949b19a260658e0287df2", "style": "IPY_MODEL_852645542b4a48518f17461d8844d156", "value": "<h4 id=\"OutlierSampleDetection_8RU75B8HZJHLPGDGVH5C4IU6E-section-CAUD6\"><b>Outlier Sample Detection</b></h4>"}}, "7c588be340e949b19a260658e0287df2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "852645542b4a48518f17461d8844d156": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "efc9dd951fef485d889ecc217078648a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_421e0efe4c74408ca0bd389b038566e4", "style": "IPY_MODEL_84e587b343de451bb1e835ae4eeac977", "value": "<p>Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/data_integrity/plot_outlier_sample_detection.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "421e0efe4c74408ca0bd389b038566e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "84e587b343de451bb1e835ae4eeac977": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b90a5b75cd004581bc133013d56ff7d8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_998f4a56ba1445e8901e3b2b9a25dd9a", "IPY_MODEL_139595f89a97451b815078a42fda0ef5", "IPY_MODEL_0289c41259e84fc6b205fc7171bebd76", "IPY_MODEL_bdf27d00623948b5b6cc5ce7355cb399", "IPY_MODEL_5390a674130c461c9e646b9c9b2aa324"], "layout": "IPY_MODEL_7c48138e745e49d38ace19fd680c5c62"}}, "998f4a56ba1445e8901e3b2b9a25dd9a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ea204cd17b6f4a20890895b83dbfd11a", "style": "IPY_MODEL_ccc6c7ffbf91494fa154e99153587075", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ea204cd17b6f4a20890895b83dbfd11a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ccc6c7ffbf91494fa154e99153587075": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "139595f89a97451b815078a42fda0ef5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a4d61b2998f94a398375f36629126740", "style": "IPY_MODEL_067ba5571b2e41508fe496fcd489c287", "value": "<div><span>\n                    The Outlier Probability Score is calculated by the LoOP algorithm which measures the local deviation\n                    of density of a given sample with respect to its neighbors. These outlier scores are directly\n                    interpretable as a probability of an object being an outlier (see\n                    <a href=\"https://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf\"\n                    target=\"_blank\" rel=\"noopener noreferrer\">link</a> for more information).<br><br>\n                    </span></div>"}}, "a4d61b2998f94a398375f36629126740": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "067ba5571b2e41508fe496fcd489c287": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0289c41259e84fc6b205fc7171bebd76": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_34526ffa8ec94204b572af752e201233", "style": "IPY_MODEL_6eb59f892d95413faffe72244986caf4", "value": "<style type=\"text/css\">\n#T_f459a_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f459a_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f459a_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f459a_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f459a_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f459a_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Outlier Probability Score</th>\n      <th class=\"col_heading level0 col1\" >target</th>\n      <th class=\"col_heading level0 col2\" >month</th>\n      <th class=\"col_heading level0 col3\" >scrape_date</th>\n      <th class=\"col_heading level0 col4\" >ext</th>\n      <th class=\"col_heading level0 col5\" >urlLength</th>\n      <th class=\"col_heading level0 col6\" >numDigits</th>\n      <th class=\"col_heading level0 col7\" >numParams</th>\n      <th class=\"col_heading level0 col8\" >num_%20</th>\n      <th class=\"col_heading level0 col9\" >num_@</th>\n      <th class=\"col_heading level0 col10\" >entropy</th>\n      <th class=\"col_heading level0 col11\" >has_ip</th>\n      <th class=\"col_heading level0 col12\" >hasHttp</th>\n      <th class=\"col_heading level0 col13\" >hasHttps</th>\n      <th class=\"col_heading level0 col14\" >urlIsLive</th>\n      <th class=\"col_heading level0 col15\" >dsr</th>\n      <th class=\"col_heading level0 col16\" >dse</th>\n      <th class=\"col_heading level0 col17\" >bodyLength</th>\n      <th class=\"col_heading level0 col18\" >numTitles</th>\n      <th class=\"col_heading level0 col19\" >numImages</th>\n      <th class=\"col_heading level0 col20\" >numLinks</th>\n      <th class=\"col_heading level0 col21\" >specialChars</th>\n      <th class=\"col_heading level0 col22\" >scriptLength</th>\n      <th class=\"col_heading level0 col23\" >sbr</th>\n      <th class=\"col_heading level0 col24\" >bscr</th>\n      <th class=\"col_heading level0 col25\" >sscr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_f459a_level0_row0\" class=\"row_heading level0 row0\" >4129</th>\n      <td id=\"T_f459a_row0_col0\" class=\"data row0 col0\" >1.00</td>\n      <td id=\"T_f459a_row0_col1\" class=\"data row0 col1\" >0</td>\n      <td id=\"T_f459a_row0_col2\" class=\"data row0 col2\" >5</td>\n      <td id=\"T_f459a_row0_col3\" class=\"data row0 col3\" >2019-05-18</td>\n      <td id=\"T_f459a_row0_col4\" class=\"data row0 col4\" >country</td>\n      <td id=\"T_f459a_row0_col5\" class=\"data row0 col5\" >128</td>\n      <td id=\"T_f459a_row0_col6\" class=\"data row0 col6\" >34</td>\n      <td id=\"T_f459a_row0_col7\" class=\"data row0 col7\" >0</td>\n      <td id=\"T_f459a_row0_col8\" class=\"data row0 col8\" >10</td>\n      <td id=\"T_f459a_row0_col9\" class=\"data row0 col9\" >0</td>\n      <td id=\"T_f459a_row0_col10\" class=\"data row0 col10\" >-4.51</td>\n      <td id=\"T_f459a_row0_col11\" class=\"data row0 col11\" >0</td>\n      <td id=\"T_f459a_row0_col12\" class=\"data row0 col12\" >True</td>\n      <td id=\"T_f459a_row0_col13\" class=\"data row0 col13\" >False</td>\n      <td id=\"T_f459a_row0_col14\" class=\"data row0 col14\" >False</td>\n      <td id=\"T_f459a_row0_col15\" class=\"data row0 col15\" >0</td>\n      <td id=\"T_f459a_row0_col16\" class=\"data row0 col16\" >0</td>\n      <td id=\"T_f459a_row0_col17\" class=\"data row0 col17\" >31701</td>\n      <td id=\"T_f459a_row0_col18\" class=\"data row0 col18\" >0</td>\n      <td id=\"T_f459a_row0_col19\" class=\"data row0 col19\" >4</td>\n      <td id=\"T_f459a_row0_col20\" class=\"data row0 col20\" >39</td>\n      <td id=\"T_f459a_row0_col21\" class=\"data row0 col21\" >5420</td>\n      <td id=\"T_f459a_row0_col22\" class=\"data row0 col22\" >794</td>\n      <td id=\"T_f459a_row0_col23\" class=\"data row0 col23\" >0.03</td>\n      <td id=\"T_f459a_row0_col24\" class=\"data row0 col24\" >0.17</td>\n      <td id=\"T_f459a_row0_col25\" class=\"data row0 col25\" >0.12</td>\n    </tr>\n    <tr>\n      <th id=\"T_f459a_level0_row1\" class=\"row_heading level0 row1\" >6505</th>\n      <td id=\"T_f459a_row1_col0\" class=\"data row1 col0\" >1.00</td>\n      <td id=\"T_f459a_row1_col1\" class=\"data row1 col1\" >0</td>\n      <td id=\"T_f459a_row1_col2\" class=\"data row1 col2\" >8</td>\n      <td id=\"T_f459a_row1_col3\" class=\"data row1 col3\" >2019-08-05</td>\n      <td id=\"T_f459a_row1_col4\" class=\"data row1 col4\" >html</td>\n      <td id=\"T_f459a_row1_col5\" class=\"data row1 col5\" >94</td>\n      <td id=\"T_f459a_row1_col6\" class=\"data row1 col6\" >7</td>\n      <td id=\"T_f459a_row1_col7\" class=\"data row1 col7\" >2</td>\n      <td id=\"T_f459a_row1_col8\" class=\"data row1 col8\" >0</td>\n      <td id=\"T_f459a_row1_col9\" class=\"data row1 col9\" >0</td>\n      <td id=\"T_f459a_row1_col10\" class=\"data row1 col10\" >-4.61</td>\n      <td id=\"T_f459a_row1_col11\" class=\"data row1 col11\" >0</td>\n      <td id=\"T_f459a_row1_col12\" class=\"data row1 col12\" >True</td>\n      <td id=\"T_f459a_row1_col13\" class=\"data row1 col13\" >False</td>\n      <td id=\"T_f459a_row1_col14\" class=\"data row1 col14\" >False</td>\n      <td id=\"T_f459a_row1_col15\" class=\"data row1 col15\" >0</td>\n      <td id=\"T_f459a_row1_col16\" class=\"data row1 col16\" >0</td>\n      <td id=\"T_f459a_row1_col17\" class=\"data row1 col17\" >282</td>\n      <td id=\"T_f459a_row1_col18\" class=\"data row1 col18\" >0</td>\n      <td id=\"T_f459a_row1_col19\" class=\"data row1 col19\" >2</td>\n      <td id=\"T_f459a_row1_col20\" class=\"data row1 col20\" >2</td>\n      <td id=\"T_f459a_row1_col21\" class=\"data row1 col21\" >167</td>\n      <td id=\"T_f459a_row1_col22\" class=\"data row1 col22\" >0</td>\n      <td id=\"T_f459a_row1_col23\" class=\"data row1 col23\" >0.00</td>\n      <td id=\"T_f459a_row1_col24\" class=\"data row1 col24\" >0.59</td>\n      <td id=\"T_f459a_row1_col25\" class=\"data row1 col25\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f459a_level0_row2\" class=\"row_heading level0 row2\" >2036</th>\n      <td id=\"T_f459a_row2_col0\" class=\"data row2 col0\" >0.99</td>\n      <td id=\"T_f459a_row2_col1\" class=\"data row2 col1\" >0</td>\n      <td id=\"T_f459a_row2_col2\" class=\"data row2 col2\" >3</td>\n      <td id=\"T_f459a_row2_col3\" class=\"data row2 col3\" >2019-03-08</td>\n      <td id=\"T_f459a_row2_col4\" class=\"data row2 col4\" >com</td>\n      <td id=\"T_f459a_row2_col5\" class=\"data row2 col5\" >103</td>\n      <td id=\"T_f459a_row2_col6\" class=\"data row2 col6\" >5</td>\n      <td id=\"T_f459a_row2_col7\" class=\"data row2 col7\" >0</td>\n      <td id=\"T_f459a_row2_col8\" class=\"data row2 col8\" >0</td>\n      <td id=\"T_f459a_row2_col9\" class=\"data row2 col9\" >0</td>\n      <td id=\"T_f459a_row2_col10\" class=\"data row2 col10\" >-4.27</td>\n      <td id=\"T_f459a_row2_col11\" class=\"data row2 col11\" >0</td>\n      <td id=\"T_f459a_row2_col12\" class=\"data row2 col12\" >True</td>\n      <td id=\"T_f459a_row2_col13\" class=\"data row2 col13\" >False</td>\n      <td id=\"T_f459a_row2_col14\" class=\"data row2 col14\" >False</td>\n      <td id=\"T_f459a_row2_col15\" class=\"data row2 col15\" >5469</td>\n      <td id=\"T_f459a_row2_col16\" class=\"data row2 col16\" >374</td>\n      <td id=\"T_f459a_row2_col17\" class=\"data row2 col17\" >43944</td>\n      <td id=\"T_f459a_row2_col18\" class=\"data row2 col18\" >1042</td>\n      <td id=\"T_f459a_row2_col19\" class=\"data row2 col19\" >526</td>\n      <td id=\"T_f459a_row2_col20\" class=\"data row2 col20\" >73</td>\n      <td id=\"T_f459a_row2_col21\" class=\"data row2 col21\" >9730</td>\n      <td id=\"T_f459a_row2_col22\" class=\"data row2 col22\" >2441</td>\n      <td id=\"T_f459a_row2_col23\" class=\"data row2 col23\" >0.06</td>\n      <td id=\"T_f459a_row2_col24\" class=\"data row2 col24\" >0.22</td>\n      <td id=\"T_f459a_row2_col25\" class=\"data row2 col25\" >0.25</td>\n    </tr>\n    <tr>\n      <th id=\"T_f459a_level0_row3\" class=\"row_heading level0 row3\" >1486</th>\n      <td id=\"T_f459a_row3_col0\" class=\"data row3 col0\" >0.99</td>\n      <td id=\"T_f459a_row3_col1\" class=\"data row3 col1\" >0</td>\n      <td id=\"T_f459a_row3_col2\" class=\"data row3 col2\" >2</td>\n      <td id=\"T_f459a_row3_col3\" class=\"data row3 col3\" >2019-02-18</td>\n      <td id=\"T_f459a_row3_col4\" class=\"data row3 col4\" >other</td>\n      <td id=\"T_f459a_row3_col5\" class=\"data row3 col5\" >181</td>\n      <td id=\"T_f459a_row3_col6\" class=\"data row3 col6\" >20</td>\n      <td id=\"T_f459a_row3_col7\" class=\"data row3 col7\" >0</td>\n      <td id=\"T_f459a_row3_col8\" class=\"data row3 col8\" >0</td>\n      <td id=\"T_f459a_row3_col9\" class=\"data row3 col9\" >2</td>\n      <td id=\"T_f459a_row3_col10\" class=\"data row3 col10\" >-4.67</td>\n      <td id=\"T_f459a_row3_col11\" class=\"data row3 col11\" >0</td>\n      <td id=\"T_f459a_row3_col12\" class=\"data row3 col12\" >True</td>\n      <td id=\"T_f459a_row3_col13\" class=\"data row3 col13\" >False</td>\n      <td id=\"T_f459a_row3_col14\" class=\"data row3 col14\" >False</td>\n      <td id=\"T_f459a_row3_col15\" class=\"data row3 col15\" >0</td>\n      <td id=\"T_f459a_row3_col16\" class=\"data row3 col16\" >0</td>\n      <td id=\"T_f459a_row3_col17\" class=\"data row3 col17\" >0</td>\n      <td id=\"T_f459a_row3_col18\" class=\"data row3 col18\" >0</td>\n      <td id=\"T_f459a_row3_col19\" class=\"data row3 col19\" >0</td>\n      <td id=\"T_f459a_row3_col20\" class=\"data row3 col20\" >0</td>\n      <td id=\"T_f459a_row3_col21\" class=\"data row3 col21\" >0</td>\n      <td id=\"T_f459a_row3_col22\" class=\"data row3 col22\" >0</td>\n      <td id=\"T_f459a_row3_col23\" class=\"data row3 col23\" >0.00</td>\n      <td id=\"T_f459a_row3_col24\" class=\"data row3 col24\" >0.00</td>\n      <td id=\"T_f459a_row3_col25\" class=\"data row3 col25\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f459a_level0_row4\" class=\"row_heading level0 row4\" >7773</th>\n      <td id=\"T_f459a_row4_col0\" class=\"data row4 col0\" >0.99</td>\n      <td id=\"T_f459a_row4_col1\" class=\"data row4 col1\" >1</td>\n      <td id=\"T_f459a_row4_col2\" class=\"data row4 col2\" >9</td>\n      <td id=\"T_f459a_row4_col3\" class=\"data row4 col3\" >2019-09-17</td>\n      <td id=\"T_f459a_row4_col4\" class=\"data row4 col4\" >country</td>\n      <td id=\"T_f459a_row4_col5\" class=\"data row4 col5\" >102</td>\n      <td id=\"T_f459a_row4_col6\" class=\"data row4 col6\" >0</td>\n      <td id=\"T_f459a_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_f459a_row4_col8\" class=\"data row4 col8\" >0</td>\n      <td id=\"T_f459a_row4_col9\" class=\"data row4 col9\" >0</td>\n      <td id=\"T_f459a_row4_col10\" class=\"data row4 col10\" >-4.62</td>\n      <td id=\"T_f459a_row4_col11\" class=\"data row4 col11\" >0</td>\n      <td id=\"T_f459a_row4_col12\" class=\"data row4 col12\" >True</td>\n      <td id=\"T_f459a_row4_col13\" class=\"data row4 col13\" >True</td>\n      <td id=\"T_f459a_row4_col14\" class=\"data row4 col14\" >False</td>\n      <td id=\"T_f459a_row4_col15\" class=\"data row4 col15\" >7800</td>\n      <td id=\"T_f459a_row4_col16\" class=\"data row4 col16\" >235</td>\n      <td id=\"T_f459a_row4_col17\" class=\"data row4 col17\" >0</td>\n      <td id=\"T_f459a_row4_col18\" class=\"data row4 col18\" >0</td>\n      <td id=\"T_f459a_row4_col19\" class=\"data row4 col19\" >0</td>\n      <td id=\"T_f459a_row4_col20\" class=\"data row4 col20\" >0</td>\n      <td id=\"T_f459a_row4_col21\" class=\"data row4 col21\" >0</td>\n      <td id=\"T_f459a_row4_col22\" class=\"data row4 col22\" >0</td>\n      <td id=\"T_f459a_row4_col23\" class=\"data row4 col23\" >0.00</td>\n      <td id=\"T_f459a_row4_col24\" class=\"data row4 col24\" >0.00</td>\n      <td id=\"T_f459a_row4_col25\" class=\"data row4 col25\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "34526ffa8ec94204b572af752e201233": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6eb59f892d95413faffe72244986caf4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bdf27d00623948b5b6cc5ce7355cb399": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a11a57b1a30a468b98c55b5577711d9e", "style": "IPY_MODEL_94faa493fd3943d7bf3e43fed25bbfb6", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 5000 samples out of 11350. 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    </body>
    </html>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 166-182

Understanding the checks' results!
==================================
Ok, so we've got some interesting results! Even though this is quite a tidy dataset
without even any preprocessing, ``deepchecks`` has found a couple of columns
(``has_ip`` and ``urlIsLive``) containing only a single value and a couple of
duplicate values.

We also get a nice list of all checks that turned out ok, and what each check is about.

So nothing dramatic, but we will be sure to drop those useless columns. :)

Preprocessing
=============
Let's split the data to train and test first. Since we want to examine how well a model
can generalize from the past to the future, we'll simply assign the first months of
the dataset to the training set, and the last few months to the test set.

.. GENERATED FROM PYTHON SOURCE LINES 182-186

.. code-block:: default


    raw_train_df = df[df.month <= 9]
    len(raw_train_df)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    8626



.. GENERATED FROM PYTHON SOURCE LINES 187-191

.. code-block:: default


    raw_test_df = df[df.month > 9]
    len(raw_test_df)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    2724



.. GENERATED FROM PYTHON SOURCE LINES 192-196

Ok! Let's process the data real quick and see how some baseline classifiers perform!

We'll just set the scrape date as our index, drop a few useless columns, one-hot
encode our categorical ext column and scale all numeric data:

.. GENERATED FROM PYTHON SOURCE LINES 196-202

.. code-block:: default


    from deepchecks.tabular.datasets.classification.phishing import \
        get_url_preprocessor

    pipeline = get_url_preprocessor()








.. GENERATED FROM PYTHON SOURCE LINES 203-204

Now we'll fit on and transform the raw train dataframe:

.. GENERATED FROM PYTHON SOURCE LINES 204-210

.. code-block:: default


    train_df = pipeline.fit_transform(raw_train_df)
    train_X = train_df.drop('target', axis=1)
    train_y = train_df['target']
    train_X.head(3)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>urlLength</th>
          <th>numDigits</th>
          <th>numParams</th>
          <th>num_%20</th>
          <th>num_@</th>
          <th>entropy</th>
          <th>hasHttp</th>
          <th>hasHttps</th>
          <th>dsr</th>
          <th>dse</th>
          <th>bodyLength</th>
          <th>numTitles</th>
          <th>numImages</th>
          <th>numLinks</th>
          <th>specialChars</th>
          <th>scriptLength</th>
          <th>sbr</th>
          <th>bscr</th>
          <th>sscr</th>
          <th>ext_com</th>
          <th>ext_country</th>
          <th>ext_html</th>
          <th>ext_info</th>
          <th>ext_net</th>
          <th>ext_other</th>
          <th>ext_php</th>
        </tr>
        <tr>
          <th>scrape_date</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
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        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2019-01-01</th>
          <td>-0.271569</td>
          <td>-0.329581</td>
          <td>-0.327303</td>
          <td>-0.089699</td>
          <td>-0.068846</td>
          <td>0.314615</td>
          <td>0.239243</td>
          <td>-0.241671</td>
          <td>0.280235</td>
          <td>-0.356485</td>
          <td>-0.125958</td>
          <td>-0.255521</td>
          <td>-0.264688</td>
          <td>1.393957</td>
          <td>-0.059321</td>
          <td>-0.068217</td>
          <td>0.753133</td>
          <td>0.753298</td>
          <td>-0.054849</td>
          <td>-0.859105</td>
          <td>-0.434899</td>
          <td>-0.401599</td>
          <td>-0.035733</td>
          <td>3.553473</td>
          <td>-0.426577</td>
          <td>-0.226517</td>
        </tr>
        <tr>
          <th>2019-01-01</th>
          <td>0.917509</td>
          <td>2.357675</td>
          <td>-0.327303</td>
          <td>5.663025</td>
          <td>-0.068846</td>
          <td>2.991389</td>
          <td>0.239243</td>
          <td>-0.241671</td>
          <td>-1.093947</td>
          <td>-0.629844</td>
          <td>-0.254032</td>
          <td>-0.344488</td>
          <td>-0.290751</td>
          <td>-0.358447</td>
          <td>-0.269256</td>
          <td>-0.282689</td>
          <td>-1.087302</td>
          <td>-0.414405</td>
          <td>-0.174310</td>
          <td>-0.859105</td>
          <td>2.299385</td>
          <td>-0.401599</td>
          <td>-0.035733</td>
          <td>-0.281415</td>
          <td>-0.426577</td>
          <td>-0.226517</td>
        </tr>
        <tr>
          <th>2019-01-01</th>
          <td>1.306246</td>
          <td>-0.484615</td>
          <td>6.957823</td>
          <td>-0.089699</td>
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          <td>1.225340</td>
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          <td>0.953338</td>
          <td>0.551243</td>
          <td>-0.061609</td>
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          <td>-0.401599</td>
          <td>-0.035733</td>
          <td>3.553473</td>
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          <td>-0.226517</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 211-213

And apply the same fitted preprocessing pipeline (with the fitted scaler, for example)
to the test dataframe:

.. GENERATED FROM PYTHON SOURCE LINES 213-219

.. code-block:: default


    test_df = pipeline.transform(raw_test_df)
    test_X = test_df.drop('target', axis=1)
    test_y = test_df['target']
    test_X.head(3)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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    <style scoped>
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    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>urlLength</th>
          <th>numDigits</th>
          <th>numParams</th>
          <th>num_%20</th>
          <th>num_@</th>
          <th>entropy</th>
          <th>hasHttp</th>
          <th>hasHttps</th>
          <th>dsr</th>
          <th>dse</th>
          <th>bodyLength</th>
          <th>numTitles</th>
          <th>numImages</th>
          <th>numLinks</th>
          <th>specialChars</th>
          <th>scriptLength</th>
          <th>sbr</th>
          <th>bscr</th>
          <th>sscr</th>
          <th>ext_com</th>
          <th>ext_country</th>
          <th>ext_html</th>
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          <th>ext_net</th>
          <th>ext_other</th>
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          <th>scrape_date</th>
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          <th>2019-10-01</th>
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          <th>2019-10-01</th>
          <td>0.002834</td>
          <td>0.238877</td>
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          <td>-0.089699</td>
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          <th>2019-10-01</th>
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          <td>0.342233</td>
          <td>-0.327303</td>
          <td>-0.089699</td>
          <td>-0.068846</td>
          <td>-0.030503</td>
          <td>0.239243</td>
          <td>-0.241671</td>
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          <td>0.020147</td>
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      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 220-223

.. code-block:: default


    from sklearn.linear_model import LogisticRegression; from sklearn.metrics import accuracy_score; hyperparameters = {'penalty': 'l2', 'fit_intercept': True, 'random_state': SEED, 'C': 0.009}








.. GENERATED FROM PYTHON SOURCE LINES 224-229

.. code-block:: default


    logreg = LogisticRegression(**hyperparameters)
    logreg.fit(train_X, train_y);
    pred_y = logreg.predict(test_X)








.. GENERATED FROM PYTHON SOURCE LINES 230-233

.. code-block:: default


    accuracy_score(test_y, pred_y)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    0.9698972099853157



.. GENERATED FROM PYTHON SOURCE LINES 234-236

Ok, so we've got a nice accuracy score from the get go! Let's see what deepchecks
can tell us about our model...

.. GENERATED FROM PYTHON SOURCE LINES 236-239

.. code-block:: default


    from deepchecks.tabular.suites import train_test_validation








.. GENERATED FROM PYTHON SOURCE LINES 240-243

.. code-block:: default


    vsuite = train_test_validation()








.. GENERATED FROM PYTHON SOURCE LINES 244-247

Now that we have separate train and test DataFrames, we will create two ``deepchecks.tabular.Dataset`` objects to enable
this suite and the next one to run addressing the train and test dataframes according to their role. Notice that here
we pass the label as a column instead of a column name, because we've seperated the feature DataFrame from the target.

.. GENERATED FROM PYTHON SOURCE LINES 247-252

.. code-block:: default


    ds_train = deepchecks.tabular.Dataset(df=train_X, label=train_y, set_datetime_from_dataframe_index=True,
                                          cat_features=[])
    ds_test = deepchecks.tabular.Dataset(df=test_X, label=test_y, set_datetime_from_dataframe_index=True, cat_features=[])





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Dataframe index has duplicate indexes, setting index to [0,1..,n-1].
    Dataframe index has duplicate indexes, setting index to [0,1..,n-1].




.. GENERATED FROM PYTHON SOURCE LINES 253-255

Now we just have to provide the ``run`` method of the suite object with both the
model and the ``Dataset`` objects.

.. GENERATED FROM PYTHON SOURCE LINES 255-258

.. code-block:: default


    vsuite.run(model=logreg, train_dataset=ds_train, test_dataset=ds_test)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Train Test Validation Suite:
    |            | 0/12 [Time: 00:00]
    Train Test Validation Suite:
    |█████       | 5/12 [Time: 00:00, Check=Date Train Test Leakage Duplicates]
    Train Test Validation Suite:
    |████████    | 8/12 [Time: 00:01, Check=Train Test Samples Mix]            
    Train Test Validation Suite:
    |█████████   | 9/12 [Time: 00:02, Check=Feature Label Correlation Change]
    Train Test Validation Suite:
    |██████████  | 10/12 [Time: 00:04, Check=Feature Drift]                  
    Train Test Validation Suite:
    |████████████| 12/12 [Time: 00:05, Check=Multivariate Drift]
                                                                                         

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        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "e8480755d3e94ca8aa4f71627d2deeb1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "212c33edf84845b9ac09c884022f50fc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ff92117ffa684eb88863398f9530d4c3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_178881af33234db1b7d227ae5f8fc049", "IPY_MODEL_e71ca4a0007b43e6a65733b7c15b14b9"], "layout": "IPY_MODEL_ee824c463c524a679359ae92044b4fd1"}}, "178881af33234db1b7d227ae5f8fc049": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f130a07788954aa7bd2a575b564f0f27", "style": "IPY_MODEL_4810b13e4be547cfab493cc331b78e22", "value": "<span id=\"summary_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\"></span>"}}, "f130a07788954aa7bd2a575b564f0f27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4810b13e4be547cfab493cc331b78e22": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e71ca4a0007b43e6a65733b7c15b14b9": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Pass"}, "children": ["IPY_MODEL_4504d9c693024bf19f9a81f894ed202c"], "layout": "IPY_MODEL_397373a7e4ed4c72b82e450949d98ada", "selected_index": null}}, "4504d9c693024bf19f9a81f894ed202c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f9f02205a82c4260b518780dc39e9b17", "IPY_MODEL_af6d13bd09b64c6caf8b17fc8eeb915c", "IPY_MODEL_77ae3b56cbef44e18899f4993dca2712", "IPY_MODEL_25f1a107e2814223b04ae5a0e9378c81", "IPY_MODEL_79b94b35f6d34379a3c28f1b70df13d3"], "layout": "IPY_MODEL_9b2399eec6c642adbec8bc115ba6098a"}}, "f9f02205a82c4260b518780dc39e9b17": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3ddcf19418d4613951560761553a858", "style": "IPY_MODEL_73c525d9bc9746ad922ab75287dce23c", "value": "<style type=\"text/css\">\n#T_31499_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31499_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31499_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31499_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31499_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_31499_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_31499_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_31499_row0_col1\" class=\"data row0 col1\" ><a href=\"#DateTrain-TestLeakage(duplicates)_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\">Date Train-Test Leakage (duplicates)</a></td>\n      <td id=\"T_31499_row0_col2\" class=\"data row0 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_31499_row0_col3\" class=\"data row0 col3\" >Found 100% leaked dates</td>\n    </tr>\n    <tr>\n      <td id=\"T_31499_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_31499_row1_col1\" class=\"data row1 col1\" ><a href=\"#DateTrain-TestLeakage(overlap)_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\">Date Train-Test Leakage (overlap)</a></td>\n      <td id=\"T_31499_row1_col2\" class=\"data row1 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_31499_row1_col3\" class=\"data row1 col3\" >Found 100% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b3ddcf19418d4613951560761553a858": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73c525d9bc9746ad922ab75287dce23c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af6d13bd09b64c6caf8b17fc8eeb915c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a4fab99dbc94412a2bce961299631d0", "style": "IPY_MODEL_da73ff9e6bca43ccb15c412a656b1738", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7a4fab99dbc94412a2bce961299631d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da73ff9e6bca43ccb15c412a656b1738": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "77ae3b56cbef44e18899f4993dca2712": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_1d7bf3e7dadc46f9b9d2e367f404fd4a", "IPY_MODEL_a99a16d0027e492fb129845214936993", "IPY_MODEL_8a8a2b80f7314800843407bfeab83aa5", "IPY_MODEL_47060d2ad4cc443d9f958c51cbdccc3e"], "layout": "IPY_MODEL_bafc2824b72248a88b42e35db51fd7e3"}}, "1d7bf3e7dadc46f9b9d2e367f404fd4a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6f2bd4c144534483999d53be65136824", "style": "IPY_MODEL_57e67b66853c47cd939d8626e7da1780", "value": "<h4 id=\"DateTrain-TestLeakage(duplicates)_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\"><b>Date Train-Test Leakage (duplicates)</b></h4>"}}, "6f2bd4c144534483999d53be65136824": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57e67b66853c47cd939d8626e7da1780": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a99a16d0027e492fb129845214936993": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_14a66b66df9c48e09694e2e8fee6f5c3", "style": "IPY_MODEL_4f85cf77d5b24581845f9d6c9c68258c", "value": "<p>Check if test dates are present in train data. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_date_train_test_leakage_duplicates.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "14a66b66df9c48e09694e2e8fee6f5c3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f85cf77d5b24581845f9d6c9c68258c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8a8a2b80f7314800843407bfeab83aa5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7f371e7acfb94be38424a89f2fc178a5", "style": "IPY_MODEL_932c6e4115be4a3ca0c2f48f0c3c6d8c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d0524_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d0524_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d0524_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d0524_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d0524_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d0524_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_d0524_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_d0524_row0_col1\" class=\"data row0 col1\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_d0524_row0_col2\" class=\"data row0 col2\" >Found 100% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7f371e7acfb94be38424a89f2fc178a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "932c6e4115be4a3ca0c2f48f0c3c6d8c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "47060d2ad4cc443d9f958c51cbdccc3e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_70239e18aa2b471f961e399d05868e63", "IPY_MODEL_6f539e6c19d24b55b90700b3f3e0da79", "IPY_MODEL_07394f6741d34aef9ec6149a60fc0ede", "IPY_MODEL_91b40e158a9a4b89ba58840944b76678"], "layout": "IPY_MODEL_9ede7d2f09034837884a5b14662a2656"}}, "70239e18aa2b471f961e399d05868e63": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_155babc47dd940d2b698fa81e8f6360b", "style": "IPY_MODEL_794cb18bcd78453faa50674df0cda87c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "155babc47dd940d2b698fa81e8f6360b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "794cb18bcd78453faa50674df0cda87c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6f539e6c19d24b55b90700b3f3e0da79": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e726c59a0d64ebabd270bbfa233a0e2", "style": "IPY_MODEL_163031ee8cd04e74afd23696e6c4ff17", "value": "<div>100% of test data dates appear in training data</div>"}}, "2e726c59a0d64ebabd270bbfa233a0e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "163031ee8cd04e74afd23696e6c4ff17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "07394f6741d34aef9ec6149a60fc0ede": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c120973787ef49cea2e573af26e5be9d", "style": "IPY_MODEL_5115b638e1614e588909ed35998fb876", "value": "<style type=\"text/css\">\n#T_9d925_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d925_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d925_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d925_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9d925_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9d925_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >duplicate values</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_9d925_level0_row0\" class=\"row_heading level0 row0\" >Sample of test dates in train:</th>\n      <td id=\"T_9d925_row0_col0\" class=\"data row0 col0\" >['1970-01-01', '1970-01-01', '1970-01-01', '1970-01-01', '1970-01-01']</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c120973787ef49cea2e573af26e5be9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5115b638e1614e588909ed35998fb876": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "91b40e158a9a4b89ba58840944b76678": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e71193af8024f8db6c965fb0a32fa81", "style": "IPY_MODEL_8331e2258ea94b048c96373ff609fe8a", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\" style=\"font-size: 14px;\">Go to top</a>"}}, "3e71193af8024f8db6c965fb0a32fa81": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8331e2258ea94b048c96373ff609fe8a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9ede7d2f09034837884a5b14662a2656": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bafc2824b72248a88b42e35db51fd7e3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25f1a107e2814223b04ae5a0e9378c81": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4126f2b25a51441d9f4d93bec2b01e34", "style": "IPY_MODEL_0fdc142b359e409bbffddfa3253ca1cb", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "4126f2b25a51441d9f4d93bec2b01e34": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0fdc142b359e409bbffddfa3253ca1cb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79b94b35f6d34379a3c28f1b70df13d3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_4a2de8bf2a0f4e1a87e099134f942fdf", "IPY_MODEL_e8a93974fea64b9fb1d886deceff9a21", "IPY_MODEL_fa58f82122f14c43b4e03832a969dccc", "IPY_MODEL_da8765158d1e4990807c1f8375488ae9"], "layout": "IPY_MODEL_34c29f0b036440cb88637fb2b1fe2802"}}, "4a2de8bf2a0f4e1a87e099134f942fdf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5845799f558427499ed4bf2c4251fef", "style": "IPY_MODEL_c1b9cce2796c4d48875ee2eb4f0f3a19", "value": "<h4 id=\"DateTrain-TestLeakage(overlap)_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\"><b>Date Train-Test Leakage (overlap)</b></h4>"}}, "a5845799f558427499ed4bf2c4251fef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c1b9cce2796c4d48875ee2eb4f0f3a19": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e8a93974fea64b9fb1d886deceff9a21": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2fa52301e70f40f0acb2d1eb886597a7", "style": "IPY_MODEL_d20720e9406a4441b4b0283620b6e113", "value": "<p>Check test data that is dated earlier than the latest date in train. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_date_train_test_leakage_overlap.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "2fa52301e70f40f0acb2d1eb886597a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d20720e9406a4441b4b0283620b6e113": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa58f82122f14c43b4e03832a969dccc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_62702ae70a184faeae46daceecace5c0", "style": "IPY_MODEL_ed33f2e1800c4a04a8243a15d8dafc47", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_99f81_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_99f81_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_99f81_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_99f81_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_99f81_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_99f81_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_99f81_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_99f81_row0_col1\" class=\"data row0 col1\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_99f81_row0_col2\" class=\"data row0 col2\" >Found 100% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "62702ae70a184faeae46daceecace5c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed33f2e1800c4a04a8243a15d8dafc47": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da8765158d1e4990807c1f8375488ae9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bf026cf30f0446ab821d4d9efb4ec297", "IPY_MODEL_28fc3a00660b43b98a7a297428916964", "IPY_MODEL_808516440b314211b71f141b5b45711e"], "layout": "IPY_MODEL_375c1dac5bb740bf85e2be24730832e5"}}, "bf026cf30f0446ab821d4d9efb4ec297": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d32ed2a9ce7f465987e31d75c18e6ebf", "style": "IPY_MODEL_01d29f81d89a44edbf9e4d0f076025c7", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d32ed2a9ce7f465987e31d75c18e6ebf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01d29f81d89a44edbf9e4d0f076025c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "28fc3a00660b43b98a7a297428916964": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1f31c9523f954be792492f8f86da8282", "style": "IPY_MODEL_1d51b5bd813a427281e9c2d34f1dea2a", "value": "<div>100% of test data samples are in the date range 1970-01-01 - 1970-01-01, which occurs before last training data date (1970-01-01)</div>"}}, "1f31c9523f954be792492f8f86da8282": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1d51b5bd813a427281e9c2d34f1dea2a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "808516440b314211b71f141b5b45711e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51b0fc04c5714c34baa2aec99ff065cb", "style": "IPY_MODEL_744a64d02cbc43b8b98746e88ee7e9b2", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-C40Z8\" style=\"font-size: 14px;\">Go to top</a>"}}, "51b0fc04c5714c34baa2aec99ff065cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "744a64d02cbc43b8b98746e88ee7e9b2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "375c1dac5bb740bf85e2be24730832e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "34c29f0b036440cb88637fb2b1fe2802": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b2399eec6c642adbec8bc115ba6098a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "397373a7e4ed4c72b82e450949d98ada": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee824c463c524a679359ae92044b4fd1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5bf879e4534d41629c57cebc139f7f80": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8adb8b5e7b0d47f5842a8fc8a8ff06f2", "IPY_MODEL_e4745dba3c28495da9a78d2732a6610e"], "layout": "IPY_MODEL_cc3a0407d05449c48dddbdfc8b740a29"}}, "8adb8b5e7b0d47f5842a8fc8a8ff06f2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcc2cb2e72b04875a9c50eaf000c437e", "style": "IPY_MODEL_43e0ed4285eb4a7ba9e987c99436731f", "value": "<span id=\"summary_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"></span>"}}, "dcc2cb2e72b04875a9c50eaf000c437e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43e0ed4285eb4a7ba9e987c99436731f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4745dba3c28495da9a78d2732a6610e": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_b3d58a2daafd4347bbf59dae6bb13875"], "layout": "IPY_MODEL_5d4d1d616fc3422d866edf592c3a198f", "selected_index": null}}, "b3d58a2daafd4347bbf59dae6bb13875": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7cc4cc6ffba3451baa42713b807bcdd6", "IPY_MODEL_90b37d55049b4cefa41aac9343e9fc94", "IPY_MODEL_d5c3345ef4d547cab43b6656511f54a5", "IPY_MODEL_d76db68296814b20a943f5e0d65bb396", "IPY_MODEL_7f5ccecd78544f2985aa899794a0741f", "IPY_MODEL_d76db68296814b20a943f5e0d65bb396", "IPY_MODEL_b51515d4a375418a8cc388574c6318cc", "IPY_MODEL_d76db68296814b20a943f5e0d65bb396", "IPY_MODEL_a403c4a3712348bf837f51c262704f0b", "IPY_MODEL_d76db68296814b20a943f5e0d65bb396", "IPY_MODEL_8ddd25df261c48b48abd99390f46a08b", "IPY_MODEL_d76db68296814b20a943f5e0d65bb396", "IPY_MODEL_e203f515eb5343d0aaab51e11fb11b3b", "IPY_MODEL_d76db68296814b20a943f5e0d65bb396", "IPY_MODEL_31b513a368a044b79b9ce9514a0d96d6"], "layout": "IPY_MODEL_afca349fb1dd4c759c60d05edfd8b6c0"}}, "7cc4cc6ffba3451baa42713b807bcdd6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21510324d2694356a9bf2fef512ecc8e", "style": "IPY_MODEL_18c1909022cf41dba62c50836579a84a", "value": "<style type=\"text/css\">\n#T_6caea_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6caea_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6caea_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6caea_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6caea_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6caea_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6caea_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Datasets Size Comparison</a></td>\n      <td id=\"T_6caea_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_6caea_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.32</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row1_col1\" class=\"data row1 col1\" ><a href=\"#NewCategoryTrainTest_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">New Category Train Test</a></td>\n      <td id=\"T_6caea_row1_col2\" class=\"data row1 col2\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_6caea_row1_col3\" class=\"data row1 col3\" >No relevant features to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row2_col1\" class=\"data row2 col1\" ><a href=\"#FeatureLabelCorrelationChange_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Feature Label Correlation Change</a></td>\n      <td id=\"T_6caea_row2_col2\" class=\"data row2 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_6caea_row2_col3\" class=\"data row2 col3\" >Passed for 26 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row3_col1\" class=\"data row3 col1\" ><a href=\"#TrainTestSamplesMix_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Train Test Samples Mix</a></td>\n      <td id=\"T_6caea_row3_col2\" class=\"data row3 col2\" >Percentage of test data samples that appear in train data is less or equal to 5%</td>\n      <td id=\"T_6caea_row3_col3\" class=\"data row3 col3\" >Percent of test data samples that appear in train data: 0.29%</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row4_col1\" class=\"data row4 col1\" ><a href=\"#FeatureLabelCorrelationChange_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Feature Label Correlation Change</a></td>\n      <td id=\"T_6caea_row4_col2\" class=\"data row4 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_6caea_row4_col3\" class=\"data row4 col3\" >Passed for 26 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row5_col1\" class=\"data row5 col1\" ><a href=\"#FeatureDrift_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Feature Drift</a></td>\n      <td id=\"T_6caea_row5_col2\" class=\"data row5 col2\" >categorical drift score < 0.2 and numerical drift score < 0.2</td>\n      <td id=\"T_6caea_row5_col3\" class=\"data row5 col3\" >Passed for 26 columns out of 26 columns.\nFound column \"numParams\" has the highest numerical drift score: 0.12</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row6_col1\" class=\"data row6 col1\" ><a href=\"#MultivariateDrift_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Multivariate Drift</a></td>\n      <td id=\"T_6caea_row6_col2\" class=\"data row6 col2\" >Drift value is less than 0.25</td>\n      <td id=\"T_6caea_row6_col3\" class=\"data row6 col3\" >Found drift value of: 0.24, corresponding to a domain classifier AUC of: 0.62</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row7_col1\" class=\"data row7 col1\" ><a href=\"#LabelDrift_06RWAP2S5T312536MTYVNLXFL-section-21FOE\">Label Drift</a></td>\n      <td id=\"T_6caea_row7_col2\" class=\"data row7 col2\" >Label drift score < 0.15</td>\n      <td id=\"T_6caea_row7_col3\" class=\"data row7 col3\" >Label's drift score Cramer's V is 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row8_col1\" class=\"data row8 col1\" >New Label Train Test</td>\n      <td id=\"T_6caea_row8_col2\" class=\"data row8 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_6caea_row8_col3\" class=\"data row8 col3\" >Found 0 new labels in test data: []</td>\n    </tr>\n    <tr>\n      <td id=\"T_6caea_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6caea_row9_col1\" class=\"data row9 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_6caea_row9_col2\" class=\"data row9 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_6caea_row9_col3\" class=\"data row9 col3\" >No relevant columns to check were found</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "21510324d2694356a9bf2fef512ecc8e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "18c1909022cf41dba62c50836579a84a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "90b37d55049b4cefa41aac9343e9fc94": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_401e3b5f70734fb29b7b1ba3b8c621da", "style": "IPY_MODEL_57e79fcf51424323a55a02531cc94584", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "401e3b5f70734fb29b7b1ba3b8c621da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57e79fcf51424323a55a02531cc94584": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d5c3345ef4d547cab43b6656511f54a5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_3152a17c89eb48a19878262412b8d628", "IPY_MODEL_0ebe51d077ce4322ace56fa74591927f", "IPY_MODEL_e8872d5fcae4422bad65e3d4823dc9e8", "IPY_MODEL_6d02708f3dbb42e988298647a2853ac1"], "layout": "IPY_MODEL_a709254170b34f8f8a71acb9a5dae173"}}, "3152a17c89eb48a19878262412b8d628": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13d0866a63614facb023b34ffad6890b", "style": "IPY_MODEL_d6708a6110004ce29aadd45cb3b62844", "value": "<h4 id=\"DatasetsSizeComparison_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>Datasets Size Comparison</b></h4>"}}, "13d0866a63614facb023b34ffad6890b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d6708a6110004ce29aadd45cb3b62844": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ebe51d077ce4322ace56fa74591927f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8ba18753acb4a25a58c68a913c60227", "style": "IPY_MODEL_4717f2390eb440eda807735b3f450249", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_datasets_size_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d8ba18753acb4a25a58c68a913c60227": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4717f2390eb440eda807735b3f450249": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e8872d5fcae4422bad65e3d4823dc9e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_84c75c70ddb54543a5339fb74f5fd06e", "style": "IPY_MODEL_e0ed89fbfcab4793b221912afc5f88cd", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e26ec_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e26ec_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e26ec_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e26ec_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e26ec_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e26ec_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e26ec_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_e26ec_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_e26ec_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.32</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "84c75c70ddb54543a5339fb74f5fd06e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e0ed89fbfcab4793b221912afc5f88cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6d02708f3dbb42e988298647a2853ac1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_77dfa709127246f3b1d24ed710504e40", "IPY_MODEL_5102520a6e994a288a3c94c271f75e78", "IPY_MODEL_ec41015d07f4422ea90a90dc08ff1c3c"], "layout": "IPY_MODEL_be377cface1a441c9161add8bb0e82a9"}}, "77dfa709127246f3b1d24ed710504e40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_765c3247d73349c6aaf99740fe314d02", "style": "IPY_MODEL_15f9ed287753423096b56a4cdb0b5827", "value": "<h5><b>Additional Outputs</b></h5>"}}, "765c3247d73349c6aaf99740fe314d02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "15f9ed287753423096b56a4cdb0b5827": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5102520a6e994a288a3c94c271f75e78": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_caa5a55d19704951b3e34029b80a33f9", "style": "IPY_MODEL_022dbc035a7c4e3f89a90a4bab1fb34f", "value": "<style type=\"text/css\">\n#T_a2aac_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2aac_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2aac_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2aac_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2aac_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a2aac_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Train</th>\n      <th class=\"col_heading level0 col1\" >Test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a2aac_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_a2aac_row0_col0\" class=\"data row0 col0\" >8626</td>\n      <td id=\"T_a2aac_row0_col1\" class=\"data row0 col1\" >2724</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "caa5a55d19704951b3e34029b80a33f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "022dbc035a7c4e3f89a90a4bab1fb34f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec41015d07f4422ea90a90dc08ff1c3c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_33e4bfd50f0c4ac1becd80ee08547f1b", "style": "IPY_MODEL_847146893b024305a07d991adde6efff", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-21FOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "33e4bfd50f0c4ac1becd80ee08547f1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "847146893b024305a07d991adde6efff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be377cface1a441c9161add8bb0e82a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a709254170b34f8f8a71acb9a5dae173": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d76db68296814b20a943f5e0d65bb396": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_477493ca0755451db2311ae76968dd0e", "style": "IPY_MODEL_ccbff19c2c6a4210842e5ec204fb3137", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "477493ca0755451db2311ae76968dd0e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ccbff19c2c6a4210842e5ec204fb3137": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f5ccecd78544f2985aa899794a0741f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_1eb65f27024a47c88b4f9dba72d37615", "IPY_MODEL_88259ba4b6b340efa78db25cab246590", "IPY_MODEL_e0d2bce43a064f6b96c0a4f40507f2d4", "IPY_MODEL_08b4521413204ca9966481c57e320e49"], "layout": "IPY_MODEL_35874069d5f84863971d12f33f58bb56"}}, "1eb65f27024a47c88b4f9dba72d37615": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8944d6838724f6197c576daf4b7648f", "style": "IPY_MODEL_fbbbff6a056e4145bfe3a7da260bc888", "value": "<h4 id=\"NewCategoryTrainTest_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>New Category Train Test</b></h4>"}}, "d8944d6838724f6197c576daf4b7648f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fbbbff6a056e4145bfe3a7da260bc888": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88259ba4b6b340efa78db25cab246590": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_91be00f0aca34138b61396b16ea80b80", "style": "IPY_MODEL_fdeb79b406bd459cb30db6d359784a27", "value": "<p>Find new categories in the test set. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_new_category_train_test.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "91be00f0aca34138b61396b16ea80b80": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fdeb79b406bd459cb30db6d359784a27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0d2bce43a064f6b96c0a4f40507f2d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0025712b1c88463e8f8cd989465619f6", "style": "IPY_MODEL_0037bc505fe24e3f9ae79fd09c499e84", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5fbce_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5fbce_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5fbce_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5fbce_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5fbce_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5fbce_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5fbce_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5fbce_row0_col1\" class=\"data row0 col1\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_5fbce_row0_col2\" class=\"data row0 col2\" >No relevant features to check were found</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0025712b1c88463e8f8cd989465619f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0037bc505fe24e3f9ae79fd09c499e84": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08b4521413204ca9966481c57e320e49": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ea0af7d4349a47c7a4f88c91b6e297dd", "IPY_MODEL_9e129b57a41c4d0fb47cb11b7eb9c227", "IPY_MODEL_06692cfb89da47c4a3eba60fceecf672"], "layout": "IPY_MODEL_0a672b95990b4bc19212c3211853b3a4"}}, "ea0af7d4349a47c7a4f88c91b6e297dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_837d11757a4e425085fd3fcbb4db5ade", "style": "IPY_MODEL_4e423bda2186460f8374f2ae2616596e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "837d11757a4e425085fd3fcbb4db5ade": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4e423bda2186460f8374f2ae2616596e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e129b57a41c4d0fb47cb11b7eb9c227": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b633924a04454798a5c88769776d6ce4", "style": "IPY_MODEL_f117b41d25b94773a24e5ad772951b02", "value": "<style type=\"text/css\">\n#T_34451_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_34451_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_34451_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_34451_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_34451_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_34451_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" ># New Categories</th>\n      <th class=\"col_heading level0 col1\" >Ratio of New Categories</th>\n      <th class=\"col_heading level0 col2\" >Feature importance</th>\n      <th class=\"col_heading level0 col3\" >New Categories Names</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Feature Name</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n"}}, "b633924a04454798a5c88769776d6ce4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f117b41d25b94773a24e5ad772951b02": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "06692cfb89da47c4a3eba60fceecf672": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_498e04572149418ca5f3a92f4070ec01", "style": "IPY_MODEL_453904410db145ad96a88f7a10a2fc5a", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-21FOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "498e04572149418ca5f3a92f4070ec01": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "453904410db145ad96a88f7a10a2fc5a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0a672b95990b4bc19212c3211853b3a4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "35874069d5f84863971d12f33f58bb56": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b51515d4a375418a8cc388574c6318cc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2725f0cca0154cf3bf873b11bc2ce6ab", "IPY_MODEL_5fd4d11d9476445fb03cc1a23cc04ea4", "IPY_MODEL_14d08e843be646218bf4a033cce81900", "IPY_MODEL_e791f6e4a4a447ed9130749defc73d2f"], "layout": "IPY_MODEL_5190ba29e7d242bfb5422b8b074e1d4d"}}, "2725f0cca0154cf3bf873b11bc2ce6ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_727100adeef44610987d27cc8409de0f", "style": "IPY_MODEL_52819ce99dc345098d2b0c02cbd3a880", "value": "<h4 id=\"TrainTestSamplesMix_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>Train Test Samples Mix</b></h4>"}}, "727100adeef44610987d27cc8409de0f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52819ce99dc345098d2b0c02cbd3a880": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5fd4d11d9476445fb03cc1a23cc04ea4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e8ce20dc1b994c43ac8d266fac0f5005", "style": "IPY_MODEL_b7de191c8ab14166b0091bc584f655de", "value": "<p>Detect samples in the test data that appear also in training data. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_train_test_samples_mix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e8ce20dc1b994c43ac8d266fac0f5005": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7de191c8ab14166b0091bc584f655de": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "14d08e843be646218bf4a033cce81900": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e8cf44212fe45fcb751e9ab81f23a9d", "style": "IPY_MODEL_41035ea2da6440e189976c2351aeb61a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_428e3_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_428e3_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_428e3_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_428e3_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_428e3_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_428e3_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_428e3_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_428e3_row0_col1\" class=\"data row0 col1\" >Percentage of test data samples that appear in train data is less or equal to 5%</td>\n      <td id=\"T_428e3_row0_col2\" class=\"data row0 col2\" >Percent of test data samples that appear in train data: 0.29%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1e8cf44212fe45fcb751e9ab81f23a9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "41035ea2da6440e189976c2351aeb61a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e791f6e4a4a447ed9130749defc73d2f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7720ef0ea5f4475c8ac46e8f5c169827", "IPY_MODEL_5d144c6ae6a54b8cb7bb353778bc2129", "IPY_MODEL_2959bcb9cd1d4b2d836047448bdbcc78", "IPY_MODEL_7f8611a58e034d719ca7a3b69e5fa43a"], "layout": "IPY_MODEL_57fedc2547e24ac4b3ea6cb86577a539"}}, "7720ef0ea5f4475c8ac46e8f5c169827": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_59f418b0e7bd4bcf802db3cf8bd052b4", "style": "IPY_MODEL_031e29245f354441b07da6d98f4ed72a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "59f418b0e7bd4bcf802db3cf8bd052b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "031e29245f354441b07da6d98f4ed72a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d144c6ae6a54b8cb7bb353778bc2129": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_233a51d7f7734301992252947e58bae8", "style": "IPY_MODEL_a6deadfcc7194f069b9fa12704c43d8b", "value": "<div>0.29% (8 / 2724)                      of test data samples appear in train data</div>"}}, "233a51d7f7734301992252947e58bae8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a6deadfcc7194f069b9fa12704c43d8b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2959bcb9cd1d4b2d836047448bdbcc78": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ec5653ba2ff443699d96cc711c3f86e5", "style": "IPY_MODEL_fc69c05e271242958f1774214d4a81b6", "value": "<style type=\"text/css\">\n#T_ecbbf_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ecbbf_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ecbbf_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ecbbf_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ecbbf_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ecbbf_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >urlLength</th>\n      <th class=\"col_heading level0 col1\" >numDigits</th>\n      <th class=\"col_heading level0 col2\" >numParams</th>\n      <th class=\"col_heading level0 col3\" >num_%20</th>\n      <th class=\"col_heading level0 col4\" >num_@</th>\n      <th class=\"col_heading level0 col5\" >entropy</th>\n      <th class=\"col_heading level0 col6\" >hasHttp</th>\n      <th class=\"col_heading level0 col7\" >hasHttps</th>\n      <th class=\"col_heading level0 col8\" >dsr</th>\n      <th class=\"col_heading level0 col9\" >dse</th>\n      <th class=\"col_heading level0 col10\" >bodyLength</th>\n      <th class=\"col_heading level0 col11\" >numTitles</th>\n      <th class=\"col_heading level0 col12\" >numImages</th>\n      <th class=\"col_heading level0 col13\" >numLinks</th>\n      <th class=\"col_heading level0 col14\" >specialChars</th>\n      <th class=\"col_heading level0 col15\" >scriptLength</th>\n      <th class=\"col_heading level0 col16\" >sbr</th>\n      <th class=\"col_heading level0 col17\" >bscr</th>\n      <th class=\"col_heading level0 col18\" >sscr</th>\n      <th class=\"col_heading level0 col19\" >ext_com</th>\n      <th class=\"col_heading level0 col20\" >ext_country</th>\n      <th class=\"col_heading level0 col21\" >ext_html</th>\n      <th class=\"col_heading level0 col22\" >ext_info</th>\n      <th class=\"col_heading level0 col23\" >ext_net</th>\n      <th class=\"col_heading level0 col24\" >ext_other</th>\n      <th class=\"col_heading level0 col25\" >ext_php</th>\n      <th class=\"col_heading level0 col26\" >target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_ecbbf_level0_row0\" class=\"row_heading level0 row0\" >Train indices: 48, 1105\nTest indices: 1468, 1747</th>\n      <td id=\"T_ecbbf_row0_col0\" class=\"data row0 col0\" >0.85</td>\n      <td id=\"T_ecbbf_row0_col1\" class=\"data row0 col1\" >-0.43</td>\n      <td id=\"T_ecbbf_row0_col2\" class=\"data row0 col2\" >2.32</td>\n      <td id=\"T_ecbbf_row0_col3\" class=\"data row0 col3\" >-0.09</td>\n      <td id=\"T_ecbbf_row0_col4\" class=\"data row0 col4\" >-0.07</td>\n      <td id=\"T_ecbbf_row0_col5\" class=\"data row0 col5\" >-1.53</td>\n      <td id=\"T_ecbbf_row0_col6\" class=\"data row0 col6\" >0.24</td>\n      <td id=\"T_ecbbf_row0_col7\" class=\"data row0 col7\" >-0.24</td>\n      <td id=\"T_ecbbf_row0_col8\" class=\"data row0 col8\" >-1.09</td>\n      <td id=\"T_ecbbf_row0_col9\" class=\"data row0 col9\" >-0.63</td>\n      <td id=\"T_ecbbf_row0_col10\" class=\"data row0 col10\" >-0.38</td>\n      <td id=\"T_ecbbf_row0_col11\" class=\"data row0 col11\" >-0.34</td>\n      <td id=\"T_ecbbf_row0_col12\" class=\"data row0 col12\" >-0.40</td>\n      <td id=\"T_ecbbf_row0_col13\" class=\"data row0 col13\" >-0.59</td>\n      <td id=\"T_ecbbf_row0_col14\" class=\"data row0 col14\" >-0.36</td>\n      <td id=\"T_ecbbf_row0_col15\" class=\"data row0 col15\" >-0.29</td>\n      <td id=\"T_ecbbf_row0_col16\" class=\"data row0 col16\" >-1.22</td>\n      <td id=\"T_ecbbf_row0_col17\" class=\"data row0 col17\" >-2.04</td>\n      <td id=\"T_ecbbf_row0_col18\" class=\"data row0 col18\" >-0.19</td>\n      <td id=\"T_ecbbf_row0_col19\" class=\"data row0 col19\" >-0.86</td>\n      <td id=\"T_ecbbf_row0_col20\" class=\"data row0 col20\" >2.30</td>\n      <td id=\"T_ecbbf_row0_col21\" class=\"data row0 col21\" >-0.40</td>\n      <td id=\"T_ecbbf_row0_col22\" class=\"data row0 col22\" >-0.04</td>\n      <td id=\"T_ecbbf_row0_col23\" class=\"data row0 col23\" >-0.28</td>\n      <td id=\"T_ecbbf_row0_col24\" class=\"data row0 col24\" >-0.43</td>\n      <td id=\"T_ecbbf_row0_col25\" class=\"data row0 col25\" >-0.23</td>\n      <td id=\"T_ecbbf_row0_col26\" class=\"data row0 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecbbf_level0_row1\" class=\"row_heading level0 row1\" >Train indices: 7976\nTest indices: 52</th>\n      <td id=\"T_ecbbf_row1_col0\" class=\"data row1 col0\" >-0.18</td>\n      <td id=\"T_ecbbf_row1_col1\" class=\"data row1 col1\" >-0.59</td>\n      <td id=\"T_ecbbf_row1_col2\" class=\"data row1 col2\" >4.97</td>\n      <td id=\"T_ecbbf_row1_col3\" class=\"data row1 col3\" >-0.09</td>\n      <td id=\"T_ecbbf_row1_col4\" class=\"data row1 col4\" >-0.07</td>\n      <td id=\"T_ecbbf_row1_col5\" class=\"data row1 col5\" >-0.03</td>\n      <td id=\"T_ecbbf_row1_col6\" class=\"data row1 col6\" >0.24</td>\n      <td id=\"T_ecbbf_row1_col7\" class=\"data row1 col7\" >-0.24</td>\n      <td id=\"T_ecbbf_row1_col8\" class=\"data row1 col8\" >-0.71</td>\n      <td id=\"T_ecbbf_row1_col9\" class=\"data row1 col9\" >-0.52</td>\n      <td id=\"T_ecbbf_row1_col10\" class=\"data row1 col10\" >-0.38</td>\n      <td id=\"T_ecbbf_row1_col11\" class=\"data row1 col11\" >-0.31</td>\n      <td id=\"T_ecbbf_row1_col12\" class=\"data row1 col12\" >-0.40</td>\n      <td id=\"T_ecbbf_row1_col13\" class=\"data row1 col13\" >-0.59</td>\n      <td id=\"T_ecbbf_row1_col14\" class=\"data row1 col14\" >-0.36</td>\n      <td id=\"T_ecbbf_row1_col15\" class=\"data row1 col15\" >-0.29</td>\n      <td id=\"T_ecbbf_row1_col16\" class=\"data row1 col16\" >-1.22</td>\n      <td id=\"T_ecbbf_row1_col17\" class=\"data row1 col17\" >-2.04</td>\n      <td id=\"T_ecbbf_row1_col18\" class=\"data row1 col18\" >-0.19</td>\n      <td id=\"T_ecbbf_row1_col19\" class=\"data row1 col19\" >-0.86</td>\n      <td id=\"T_ecbbf_row1_col20\" class=\"data row1 col20\" >-0.43</td>\n      <td id=\"T_ecbbf_row1_col21\" class=\"data row1 col21\" >-0.40</td>\n      <td id=\"T_ecbbf_row1_col22\" class=\"data row1 col22\" >-0.04</td>\n      <td id=\"T_ecbbf_row1_col23\" class=\"data row1 col23\" >-0.28</td>\n      <td id=\"T_ecbbf_row1_col24\" class=\"data row1 col24\" >-0.43</td>\n      <td id=\"T_ecbbf_row1_col25\" class=\"data row1 col25\" >4.41</td>\n      <td id=\"T_ecbbf_row1_col26\" class=\"data row1 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecbbf_level0_row2\" class=\"row_heading level0 row2\" >Train indices: 174\nTest indices: 1059</th>\n      <td id=\"T_ecbbf_row2_col0\" class=\"data row2 col0\" >-0.41</td>\n      <td id=\"T_ecbbf_row2_col1\" class=\"data row2 col1\" >-0.02</td>\n      <td id=\"T_ecbbf_row2_col2\" class=\"data row2 col2\" >2.98</td>\n      <td id=\"T_ecbbf_row2_col3\" class=\"data row2 col3\" >-0.09</td>\n      <td id=\"T_ecbbf_row2_col4\" class=\"data row2 col4\" >-0.07</td>\n      <td id=\"T_ecbbf_row2_col5\" class=\"data row2 col5\" >-0.42</td>\n      <td id=\"T_ecbbf_row2_col6\" class=\"data row2 col6\" >0.24</td>\n      <td id=\"T_ecbbf_row2_col7\" class=\"data row2 col7\" >-0.24</td>\n      <td id=\"T_ecbbf_row2_col8\" class=\"data row2 col8\" >-1.09</td>\n      <td id=\"T_ecbbf_row2_col9\" class=\"data row2 col9\" >-0.63</td>\n      <td id=\"T_ecbbf_row2_col10\" class=\"data row2 col10\" >-0.38</td>\n      <td id=\"T_ecbbf_row2_col11\" class=\"data row2 col11\" >-0.34</td>\n      <td id=\"T_ecbbf_row2_col12\" class=\"data row2 col12\" >-0.40</td>\n      <td id=\"T_ecbbf_row2_col13\" class=\"data row2 col13\" >-0.59</td>\n      <td id=\"T_ecbbf_row2_col14\" class=\"data row2 col14\" >-0.36</td>\n      <td id=\"T_ecbbf_row2_col15\" class=\"data row2 col15\" >-0.29</td>\n      <td id=\"T_ecbbf_row2_col16\" class=\"data row2 col16\" >-1.22</td>\n      <td id=\"T_ecbbf_row2_col17\" class=\"data row2 col17\" >-2.04</td>\n      <td id=\"T_ecbbf_row2_col18\" class=\"data row2 col18\" >-0.19</td>\n      <td id=\"T_ecbbf_row2_col19\" class=\"data row2 col19\" >-0.86</td>\n      <td id=\"T_ecbbf_row2_col20\" class=\"data row2 col20\" >-0.43</td>\n      <td id=\"T_ecbbf_row2_col21\" class=\"data row2 col21\" >-0.40</td>\n      <td id=\"T_ecbbf_row2_col22\" class=\"data row2 col22\" >-0.04</td>\n      <td id=\"T_ecbbf_row2_col23\" class=\"data row2 col23\" >-0.28</td>\n      <td id=\"T_ecbbf_row2_col24\" class=\"data row2 col24\" >-0.43</td>\n      <td id=\"T_ecbbf_row2_col25\" class=\"data row2 col25\" >4.41</td>\n      <td id=\"T_ecbbf_row2_col26\" class=\"data row2 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecbbf_level0_row3\" class=\"row_heading level0 row3\" >Train indices: 6774\nTest indices: 1874</th>\n      <td id=\"T_ecbbf_row3_col0\" class=\"data row3 col0\" >-0.09</td>\n      <td id=\"T_ecbbf_row3_col1\" class=\"data row3 col1\" >-0.54</td>\n      <td id=\"T_ecbbf_row3_col2\" class=\"data row3 col2\" >4.97</td>\n      <td id=\"T_ecbbf_row3_col3\" class=\"data row3 col3\" >-0.09</td>\n      <td id=\"T_ecbbf_row3_col4\" class=\"data row3 col4\" >-0.07</td>\n      <td id=\"T_ecbbf_row3_col5\" class=\"data row3 col5\" >-0.04</td>\n      <td id=\"T_ecbbf_row3_col6\" class=\"data row3 col6\" >0.24</td>\n      <td id=\"T_ecbbf_row3_col7\" class=\"data row3 col7\" >-0.24</td>\n      <td id=\"T_ecbbf_row3_col8\" class=\"data row3 col8\" >-0.71</td>\n      <td id=\"T_ecbbf_row3_col9\" class=\"data row3 col9\" >-0.52</td>\n      <td id=\"T_ecbbf_row3_col10\" class=\"data row3 col10\" >-0.38</td>\n      <td id=\"T_ecbbf_row3_col11\" class=\"data row3 col11\" >-0.31</td>\n      <td id=\"T_ecbbf_row3_col12\" class=\"data row3 col12\" >-0.40</td>\n      <td id=\"T_ecbbf_row3_col13\" class=\"data row3 col13\" >-0.59</td>\n      <td id=\"T_ecbbf_row3_col14\" class=\"data row3 col14\" >-0.36</td>\n      <td id=\"T_ecbbf_row3_col15\" class=\"data row3 col15\" >-0.29</td>\n      <td id=\"T_ecbbf_row3_col16\" class=\"data row3 col16\" >-1.22</td>\n      <td id=\"T_ecbbf_row3_col17\" class=\"data row3 col17\" >-2.04</td>\n      <td id=\"T_ecbbf_row3_col18\" class=\"data row3 col18\" >-0.19</td>\n      <td id=\"T_ecbbf_row3_col19\" class=\"data row3 col19\" >-0.86</td>\n      <td id=\"T_ecbbf_row3_col20\" class=\"data row3 col20\" >-0.43</td>\n      <td id=\"T_ecbbf_row3_col21\" class=\"data row3 col21\" >-0.40</td>\n      <td id=\"T_ecbbf_row3_col22\" class=\"data row3 col22\" >-0.04</td>\n      <td id=\"T_ecbbf_row3_col23\" class=\"data row3 col23\" >-0.28</td>\n      <td id=\"T_ecbbf_row3_col24\" class=\"data row3 col24\" >-0.43</td>\n      <td id=\"T_ecbbf_row3_col25\" class=\"data row3 col25\" >4.41</td>\n      <td id=\"T_ecbbf_row3_col26\" class=\"data row3 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecbbf_level0_row4\" class=\"row_heading level0 row4\" >Train indices: 6437, 4974, 4083, 2983\nTest indices: 1888</th>\n      <td id=\"T_ecbbf_row4_col0\" class=\"data row4 col0\" >0.21</td>\n      <td id=\"T_ecbbf_row4_col1\" class=\"data row4 col1\" >0.70</td>\n      <td id=\"T_ecbbf_row4_col2\" class=\"data row4 col2\" >2.32</td>\n      <td id=\"T_ecbbf_row4_col3\" class=\"data row4 col3\" >-0.09</td>\n      <td id=\"T_ecbbf_row4_col4\" class=\"data row4 col4\" >-0.07</td>\n      <td id=\"T_ecbbf_row4_col5\" class=\"data row4 col5\" >-1.40</td>\n      <td id=\"T_ecbbf_row4_col6\" class=\"data row4 col6\" >0.24</td>\n      <td id=\"T_ecbbf_row4_col7\" class=\"data row4 col7\" >-0.24</td>\n      <td id=\"T_ecbbf_row4_col8\" class=\"data row4 col8\" >-1.09</td>\n      <td id=\"T_ecbbf_row4_col9\" class=\"data row4 col9\" >-0.63</td>\n      <td id=\"T_ecbbf_row4_col10\" class=\"data row4 col10\" >-0.38</td>\n      <td id=\"T_ecbbf_row4_col11\" class=\"data row4 col11\" >-0.34</td>\n      <td id=\"T_ecbbf_row4_col12\" class=\"data row4 col12\" >-0.40</td>\n      <td id=\"T_ecbbf_row4_col13\" class=\"data row4 col13\" >-0.59</td>\n      <td id=\"T_ecbbf_row4_col14\" class=\"data row4 col14\" >-0.36</td>\n      <td id=\"T_ecbbf_row4_col15\" class=\"data row4 col15\" >-0.29</td>\n      <td id=\"T_ecbbf_row4_col16\" class=\"data row4 col16\" >-1.22</td>\n      <td id=\"T_ecbbf_row4_col17\" class=\"data row4 col17\" >-2.04</td>\n      <td id=\"T_ecbbf_row4_col18\" class=\"data row4 col18\" >-0.19</td>\n      <td id=\"T_ecbbf_row4_col19\" class=\"data row4 col19\" >-0.86</td>\n      <td id=\"T_ecbbf_row4_col20\" class=\"data row4 col20\" >-0.43</td>\n      <td id=\"T_ecbbf_row4_col21\" class=\"data row4 col21\" >-0.40</td>\n      <td id=\"T_ecbbf_row4_col22\" class=\"data row4 col22\" >-0.04</td>\n      <td id=\"T_ecbbf_row4_col23\" class=\"data row4 col23\" >-0.28</td>\n      <td id=\"T_ecbbf_row4_col24\" class=\"data row4 col24\" >2.34</td>\n      <td id=\"T_ecbbf_row4_col25\" class=\"data row4 col25\" >-0.23</td>\n      <td id=\"T_ecbbf_row4_col26\" class=\"data row4 col26\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ec5653ba2ff443699d96cc711c3f86e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc69c05e271242958f1774214d4a81b6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f8611a58e034d719ca7a3b69e5fa43a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_02795df9b4374f1b911305f2b34a66d7", "style": "IPY_MODEL_fcff9dd7550a40e6ad58ad55d76cd63a", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-21FOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "02795df9b4374f1b911305f2b34a66d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fcff9dd7550a40e6ad58ad55d76cd63a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57fedc2547e24ac4b3ea6cb86577a539": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5190ba29e7d242bfb5422b8b074e1d4d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a403c4a3712348bf837f51c262704f0b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_af3d2cad15d3437d83437196a811b0d1", "IPY_MODEL_cd3419ad4e71408696572bcbc6f24223", "IPY_MODEL_7be4d6a9dd4f490492679452383a4d14", "IPY_MODEL_5150b652b586433991f2c04581e6f162"], "layout": "IPY_MODEL_73f2bab9c9e640ad9832545ebf2fbb73"}}, "af3d2cad15d3437d83437196a811b0d1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f73e56ef3bd84b01b758e27a16a83b74", "style": "IPY_MODEL_7d3e27202c1449baa1cec18ea22ef423", "value": "<h4 id=\"FeatureLabelCorrelationChange_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>Feature Label Correlation Change</b></h4>"}}, "f73e56ef3bd84b01b758e27a16a83b74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d3e27202c1449baa1cec18ea22ef423": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cd3419ad4e71408696572bcbc6f24223": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8409f82662934c058200794bbbd5b0e3", "style": "IPY_MODEL_9d4b84f478444f8b9048142601bfd7c3", "value": "<p>    Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8409f82662934c058200794bbbd5b0e3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d4b84f478444f8b9048142601bfd7c3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7be4d6a9dd4f490492679452383a4d14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_43d9a176bc8e411f974b1d83f6a7eea0", "style": "IPY_MODEL_29c29cff40ae4d2489106f33c8d76908", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6e00e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e00e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e00e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e00e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e00e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6e00e_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6e00e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6e00e_row0_col1\" class=\"data row0 col1\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_6e00e_row0_col2\" class=\"data row0 col2\" >Passed for 26 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_6e00e_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6e00e_row1_col1\" class=\"data row1 col1\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_6e00e_row1_col2\" class=\"data row1 col2\" >Passed for 26 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "43d9a176bc8e411f974b1d83f6a7eea0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "29c29cff40ae4d2489106f33c8d76908": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5150b652b586433991f2c04581e6f162": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_96fffaaeed8f4295a5941617da2d8714", "IPY_MODEL_78cceb1aeb074e52bf4b5dc18ec28ff9", "IPY_MODEL_a4ff3df753d94fe0b561b331b6e8f625", "IPY_MODEL_c0ced5b8315f486c8e1b4e81ba8e9cfc", "IPY_MODEL_25393e8aca3f4c36a7ba4e2b27269e50", "IPY_MODEL_16ed7cb3bdf54f7b99fb4f0feea01eae", "IPY_MODEL_35baa4930a9f449490aee0b3b6d8c454", "IPY_MODEL_3bfb0c5022854d09b228e0f2c3d82012", "IPY_MODEL_ec20c3401d4745f0bfe566069834d49b", "IPY_MODEL_7ae746571d064726906ea23925a78d71", "IPY_MODEL_d5e8822ef63c441aaea2b93a465570a6", "IPY_MODEL_dc21353a7ca64494ae6707b26a5aca24", "IPY_MODEL_be86d40f35244e9dbc0be5b7a4cc8396"], "layout": "IPY_MODEL_59c99c82621649da8e5d20eb726030c3"}}, "96fffaaeed8f4295a5941617da2d8714": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_23fcdfdb58d54c009854b8641e615162", "style": "IPY_MODEL_ee37723730d04ae49548dfbc377e4175", "value": "<h5><b>Additional Outputs</b></h5>"}}, "23fcdfdb58d54c009854b8641e615162": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee37723730d04ae49548dfbc377e4175": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "78cceb1aeb074e52bf4b5dc18ec28ff9": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.11</b>", "<b>0.33</b>", "<b>0.1</b>", "<b>0.11</b>", "<b>0.02</b>"], "textposition": "outside", "x": ["bscr", "dsr", "bodyLength", "specialChars", "sbr"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "4082c092-c384-4280-8bc8-cc4ba59f669f"}, {"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>0.0</b>", "<b>0.23</b>", "<b>0.0</b>", "<b>0.15</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["bscr", "dsr", "bodyLength", "specialChars", "sbr"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "387aed4b-bfb8-47fa-a50c-8aa91924d74d"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": 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{"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 7], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "4rqG3mCXvD8fxn4NHg7VP3Q89JociLg/+w+fyuzHvD85PFFCYRGXPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "OIocuU7YGz/pV5GXidnMPwAAAAAAAAAA53rPIXSGwz8cKcNAOcDnPg=="}]}, "a4ff3df753d94fe0b561b331b6e8f625": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8490dc749175428d92c549f377dc0da3", "style": "IPY_MODEL_514d4505fc1a427782134b6a63e1b7f4", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "8490dc749175428d92c549f377dc0da3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "514d4505fc1a427782134b6a63e1b7f4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c0ced5b8315f486c8e1b4e81ba8e9cfc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e0fbbd4f6dc94c58987245379ab9aead", "style": "IPY_MODEL_e2147d60efd84078a6cbfbb0a1846409", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "e0fbbd4f6dc94c58987245379ab9aead": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e2147d60efd84078a6cbfbb0a1846409": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "25393e8aca3f4c36a7ba4e2b27269e50": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f5d8b91286fa48c2b1fa07458990ca0c", "style": "IPY_MODEL_7d33126f3cf14f8da7eca7fb1ec2a7fd", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "f5d8b91286fa48c2b1fa07458990ca0c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d33126f3cf14f8da7eca7fb1ec2a7fd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "16ed7cb3bdf54f7b99fb4f0feea01eae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_df9f238b1a8447e7804146a121115a5e", "style": "IPY_MODEL_7e0b96ade3c94521a34fbaf106070315", "value": "<div>Can indicate that this feature's success in predicting the label is actually due to data leakage, </div>"}}, "df9f238b1a8447e7804146a121115a5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e0b96ade3c94521a34fbaf106070315": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "35baa4930a9f449490aee0b3b6d8c454": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b24a3073d6b49bbacb9c655b808c68a", "style": "IPY_MODEL_da94cd81217b42c5861dc56d9673c1d8", "value": "<div>   meaning that the feature holds information that is based on the label to begin with.</div>"}}, "1b24a3073d6b49bbacb9c655b808c68a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da94cd81217b42c5861dc56d9673c1d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3bfb0c5022854d09b228e0f2c3d82012": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ed1d7c6bfc754221878353eb07585aa8", "style": "IPY_MODEL_d85846189eed431096635398dc9e9f06", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "ed1d7c6bfc754221878353eb07585aa8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d85846189eed431096635398dc9e9f06": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec20c3401d4745f0bfe566069834d49b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_672b3ee43e7d4776a4723ff46c2c8341", "style": "IPY_MODEL_e951d32eab0b4454859004246bde6cb8", "value": "<div>   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test </div>"}}, "672b3ee43e7d4776a4723ff46c2c8341": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e951d32eab0b4454859004246bde6cb8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7ae746571d064726906ea23925a78d71": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_befdc0478b7140daa37f533e49400959", "style": "IPY_MODEL_5e2d9df244fa44cc95645a775b0ae1ae", "value": "<div>   can be explained by leakage in train that is not relevant to a new dataset.</div>"}}, "befdc0478b7140daa37f533e49400959": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e2d9df244fa44cc95645a775b0ae1ae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d5e8822ef63c441aaea2b93a465570a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_663b6de0bf944850944f697f84f2313d", "style": "IPY_MODEL_0b588edb83564a548afdcd86ffe58d4e", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "663b6de0bf944850944f697f84f2313d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0b588edb83564a548afdcd86ffe58d4e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dc21353a7ca64494ae6707b26a5aca24": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bc996e5831504eb2949d69b895547f15", "style": "IPY_MODEL_8166eebb0d0c40a6a3d68da58e589225", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "bc996e5831504eb2949d69b895547f15": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8166eebb0d0c40a6a3d68da58e589225": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be86d40f35244e9dbc0be5b7a4cc8396": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_331892264bce43e8b07b6ce35fc2f4f6", "style": "IPY_MODEL_8eb6142f178242519783e408b7784994", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-21FOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "331892264bce43e8b07b6ce35fc2f4f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8eb6142f178242519783e408b7784994": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59c99c82621649da8e5d20eb726030c3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73f2bab9c9e640ad9832545ebf2fbb73": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ddd25df261c48b48abd99390f46a08b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_97e30250972e4309b1c0f6bdd2561e95", "IPY_MODEL_08e6f5dcabc54e759139b6f995f971b6", "IPY_MODEL_057b42bf702941e0a21638ee966cff54", "IPY_MODEL_af86e05de46641f8a08a7c5364c0f077"], "layout": "IPY_MODEL_5696c3e81f4643898ae96fbb36a7ca8c"}}, "97e30250972e4309b1c0f6bdd2561e95": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f3d211d5e9f4ea691cbf8a5ffcea6da", "style": "IPY_MODEL_b091f99e8c3a48fbb3a523d6a3d20108", "value": "<h4 id=\"FeatureDrift_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>Feature Drift</b></h4>"}}, "5f3d211d5e9f4ea691cbf8a5ffcea6da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b091f99e8c3a48fbb3a523d6a3d20108": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "08e6f5dcabc54e759139b6f995f971b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8c6434cfbe6242ba82da7fb6ff9ef93c", "style": "IPY_MODEL_42b9deac560c4ffd9e9248c1853d53c6", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8c6434cfbe6242ba82da7fb6ff9ef93c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42b9deac560c4ffd9e9248c1853d53c6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "057b42bf702941e0a21638ee966cff54": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_37086d986b17413ea1b4d370439fb7b4", "style": "IPY_MODEL_9b88f3a1f2544de3a57bf85cf8bb6aba", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_af0e8_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_af0e8_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_af0e8_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_af0e8_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_af0e8_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_af0e8_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_af0e8_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_af0e8_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.2</td>\n      <td id=\"T_af0e8_row0_col2\" class=\"data row0 col2\" >Passed for 26 columns out of 26 columns.\nFound column \"numParams\" has the highest numerical drift score: 0.12</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "37086d986b17413ea1b4d370439fb7b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b88f3a1f2544de3a57bf85cf8bb6aba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af86e05de46641f8a08a7c5364c0f077": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_35ddf2f8161f48da9ed69eeddf75bbdf", "IPY_MODEL_0141b9d731eb4e5e8a6a953ec183b60e", "IPY_MODEL_c5fa9273bdf44ba08ec105cdaf395790", "IPY_MODEL_b5a641c50d0a4aca9b55db587ebe625d", "IPY_MODEL_d87947a68f5c4a2b932890e9dfeca92d", "IPY_MODEL_674e8e1d99484c4ab5a50daf41a7c30e", "IPY_MODEL_455a0cf1b0b04be6b24abfa2568d21f5", "IPY_MODEL_24159760e5414a92bdf9eb5e1e79ae5e", "IPY_MODEL_7ae1316ea24341deba430905b85174a7", "IPY_MODEL_c845ea3f6f3a4b3b90cc973e8842e775"], "layout": "IPY_MODEL_6cc92bd51b0c48c1bb3c919d0ab2db27"}}, "35ddf2f8161f48da9ed69eeddf75bbdf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e3714aa951d4161a01b9347287adfff", "style": "IPY_MODEL_8dfb436cc0974c57b660eca0e7a5d5ea", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4e3714aa951d4161a01b9347287adfff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8dfb436cc0974c57b660eca0e7a5d5ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0141b9d731eb4e5e8a6a953ec183b60e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1db572cafefe47dea3db44e533626eeb", "style": "IPY_MODEL_fb1f0547dcaf462a899701d287383004", "value": "<div>\n<span>\nThe Drift score is a measure for the difference between two distributions, in this check - the test\nand train distributions.<br> The check shows the drift score and distributions for the features,\nsorted by the sum of the drift score and the feature importance and showing only the top 5 features,\naccording to the sum of the drift score and the feature importance.\n</span>\n</div>"}}, "1db572cafefe47dea3db44e533626eeb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb1f0547dcaf462a899701d287383004": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c5fa9273bdf44ba08ec105cdaf395790": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6eb18a1d569d4f54a73fb83fdb5577a0", "style": "IPY_MODEL_0afb78f333984207920b15a635716cbe", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "6eb18a1d569d4f54a73fb83fdb5577a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0afb78f333984207920b15a635716cbe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b5a641c50d0a4aca9b55db587ebe625d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_194f171d747d480297692eeae0cf1f88", "style": "IPY_MODEL_99b8e4e292d94407b73009e463b03452", "value": "<div>If available, the plot titles also show the feature importance (FI) rank</div>"}}, "194f171d747d480297692eeae0cf1f88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "99b8e4e292d94407b73009e463b03452": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d87947a68f5c4a2b932890e9dfeca92d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "500dfa30-3dd8-4582-9425-11e9026c8129"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.016237730891104335], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "b05bfddd-1afc-4044-a5dd-e66034e74533"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": {"dtype": "float64", "shape": [12]}, "xaxis": "x2", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y2", "type": "bar", "uid": "4a108fdd-397e-45e5-be7f-e086ca07882f"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": {"dtype": "float64", "shape": [12]}, "xaxis": "x2", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y2", "type": "bar", "uid": "5a1842da-6ce5-41d3-a615-4ecb8aa5ccb4"}], 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"model_module_version": "1.2.0", "state": {}}, "e203f515eb5343d0aaab51e11fb11b3b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_7e6b66d008104703b717ccc3ad52885a", "IPY_MODEL_56acf2000353439690709a950ca951b6", "IPY_MODEL_bf05836a6a7a4a029fb00d3911e271fa", "IPY_MODEL_6532f5c0cd984a8fa0104d5c5beb9bcb"], "layout": "IPY_MODEL_884d368f3f9b44e7a070791f5b6577a5"}}, "7e6b66d008104703b717ccc3ad52885a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_144f395470e043dc8f427b3b6bbdb059", "style": "IPY_MODEL_350e37f9b5ea46ddabba3d3a755c148d", "value": "<h4 id=\"LabelDrift_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>Label Drift</b></h4>"}}, "144f395470e043dc8f427b3b6bbdb059": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "350e37f9b5ea46ddabba3d3a755c148d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "56acf2000353439690709a950ca951b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c6488d251b4843ee8ca1c7d14a6949bf", "style": "IPY_MODEL_37f18e84bedb4a1a87e36eb635d16710", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c6488d251b4843ee8ca1c7d14a6949bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37f18e84bedb4a1a87e36eb635d16710": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf05836a6a7a4a029fb00d3911e271fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_97654e38b4064da8aa6d69df1a4919d7", "style": "IPY_MODEL_303fcfec3d2b492987b671b05b12353f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f7cd0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f7cd0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f7cd0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f7cd0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f7cd0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f7cd0_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f7cd0_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f7cd0_row0_col1\" class=\"data row0 col1\" >Label drift score < 0.15</td>\n      <td id=\"T_f7cd0_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "97654e38b4064da8aa6d69df1a4919d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "303fcfec3d2b492987b671b05b12353f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6532f5c0cd984a8fa0104d5c5beb9bcb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f2df30cac8b9421b9204762059c6c272", "IPY_MODEL_73b0a693a9394b1fa7eb7ef6a82ff7ec", "IPY_MODEL_09754dad03374c739213a9c0a5872800", "IPY_MODEL_cfff32ca2d014adf83fb0f331825939e", "IPY_MODEL_0e9704b5d2204ed1814637573c893338"], "layout": "IPY_MODEL_cb05f205d4c84021960c852265e6e51c"}}, "f2df30cac8b9421b9204762059c6c272": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dc35a9b1f87b49cf845623ed31214d13", "style": "IPY_MODEL_724e0160eb604749bbee7f5af4b2641e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "dc35a9b1f87b49cf845623ed31214d13": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "724e0160eb604749bbee7f5af4b2641e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "73b0a693a9394b1fa7eb7ef6a82ff7ec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_727c9427e0a349e6a1d1e18ed60900dc", "style": "IPY_MODEL_11b8fa00d30d44429970210d835a933b", "value": "<div><span>\n                        The Drift score is a measure for the difference between two distributions, in this check -\n                        the test and train distributions.<br> The check shows the drift score\n                        and distributions for the label. </span></div>"}}, "727c9427e0a349e6a1d1e18ed60900dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "11b8fa00d30d44429970210d835a933b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "09754dad03374c739213a9c0a5872800": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5956c305b81042c091332a199b56b8d3", "style": "IPY_MODEL_b2d5c1eaac7f490bb4110ebbb5caf233", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "5956c305b81042c091332a199b56b8d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b2d5c1eaac7f490bb4110ebbb5caf233": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cfff32ca2d014adf83fb0f331825939e": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": 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"range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 4], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "dFNSHWID7z97kbVVvJOfPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "dFNSHWID7z97kbVVvJOfPw=="}]}, "0e9704b5d2204ed1814637573c893338": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_89bf971cda6744558db58f95ed565b28", "style": "IPY_MODEL_d057297d736242fca3c49868eaeb7655", "value": "<br><a href=\"#summary_06RWAP2S5T312536MTYVNLXFL-section-21FOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "89bf971cda6744558db58f95ed565b28": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d057297d736242fca3c49868eaeb7655": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb05f205d4c84021960c852265e6e51c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "884d368f3f9b44e7a070791f5b6577a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "31b513a368a044b79b9ce9514a0d96d6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_4e0abae9065d42f3ac048b4b27da0144", "IPY_MODEL_81f93d4fc8a8413096ffe680b705fa32", "IPY_MODEL_6d7aea77e8174928b53d3b778070edc4", "IPY_MODEL_8321a59086174068983f20c569ba3df8"], "layout": "IPY_MODEL_f72b83e65aba4710a6c8df5bc7ec471d"}}, "4e0abae9065d42f3ac048b4b27da0144": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5fad8f95cfcb447eb47b7f27d2755cec", "style": "IPY_MODEL_9dfd1b1d69424a7b85b9a3d438c013b5", "value": "<h4 id=\"MultivariateDrift_06RWAP2S5T312536MTYVNLXFL-section-21FOE\"><b>Multivariate Drift</b></h4>"}}, "5fad8f95cfcb447eb47b7f27d2755cec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9dfd1b1d69424a7b85b9a3d438c013b5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "81f93d4fc8a8413096ffe680b705fa32": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f6cbbe3f15d4e598368f8adebe33458", "style": "IPY_MODEL_f917cb8d7e8e4f7a8b6c2e8e3f8cbfe3", "value": "<p>    Calculate drift between the entire train and test datasets using a model trained to distinguish between them. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_multivariate_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4f6cbbe3f15d4e598368f8adebe33458": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f917cb8d7e8e4f7a8b6c2e8e3f8cbfe3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6d7aea77e8174928b53d3b778070edc4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dd835693cae647498423ef73a920c541", "style": "IPY_MODEL_88a010f8fa70418d8bdf17a8846b7096", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f874f_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f874f_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f874f_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f874f_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f874f_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f874f_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f874f_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f874f_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.25</td>\n      <td id=\"T_f874f_row0_col2\" class=\"data row0 col2\" >Found drift value of: 0.24, corresponding to a domain classifier AUC of: 0.62</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "dd835693cae647498423ef73a920c541": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88a010f8fa70418d8bdf17a8846b7096": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8321a59086174068983f20c569ba3df8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e9b4b13765c949afaa52c005e08417f5", "IPY_MODEL_94b66988b96143cfbabe30956e743f9c", "IPY_MODEL_4aaab3e6fcea4d39b81edf69b35c1ee2", "IPY_MODEL_2831dc735e2b41ea99a48bb3e00f15db", "IPY_MODEL_af52ed69eacd4d96809d757fe0d8a247", "IPY_MODEL_e7e84fc2a5224191a053b8ff9d96b47c", "IPY_MODEL_7091bee89f94485eb3c5cb69c156cf0e", "IPY_MODEL_d0879079400b4deebb1a803b1b49bd15", "IPY_MODEL_dfa7106059b44298ae464281cae77ff4", "IPY_MODEL_b3b5a4dc59984a8387cde29afc87db64", "IPY_MODEL_4ad8c04856ef49fc9c69421caf40f9b4"], "layout": "IPY_MODEL_59db43e1b2944b49a254ad9197e718af"}}, "e9b4b13765c949afaa52c005e08417f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d0f23a3b4aac461bbe40fcdfcba24283", "style": "IPY_MODEL_e67bc819bf054d1f8fedd0b8dd93b75a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d0f23a3b4aac461bbe40fcdfcba24283": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e67bc819bf054d1f8fedd0b8dd93b75a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "94b66988b96143cfbabe30956e743f9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_997bc336f4f34cb6b941cce0bf4a44c0", "style": "IPY_MODEL_00a5d3fe97b1415fbba969c046248a9a", "value": "<div>\n        <span>\n        The shown features are the features that are most important for the domain classifier - the\n        domain_classifier trained to distinguish between the train and test datasets.<br>\n        </span>\n        </div>"}}, "997bc336f4f34cb6b941cce0bf4a44c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "00a5d3fe97b1415fbba969c046248a9a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4aaab3e6fcea4d39b81edf69b35c1ee2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ee88503a80c04da9a1385c244c2f8f47", "style": "IPY_MODEL_eb75906b9ede4f1d86697fb3e79ceb1a", "value": "<div>\n    <span>\n    The percents of explained dataset difference are the importance values for the feature calculated\n    using `permutation_importance`.\n    </span><br><br>\n    </div>"}}, "ee88503a80c04da9a1385c244c2f8f47": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eb75906b9ede4f1d86697fb3e79ceb1a": {"model_name": "DescriptionStyleModel", "model_module": 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"rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "af52ed69eacd4d96809d757fe0d8a247": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bbca3c221cfc40e09d0b197048a610fb", "style": "IPY_MODEL_a4d1658bdf704e37ae95b31bb9c7c7bd", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "bbca3c221cfc40e09d0b197048a610fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a4d1658bdf704e37ae95b31bb9c7c7bd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e7e84fc2a5224191a053b8ff9d96b47c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13d671e62ee44286a218dd13d43f2037", "style": "IPY_MODEL_6abceb64bd5f495ea6446f138c0ceec9", "value": "<div>Showing only the top 3 columns, you can change it using n_top_columns param</div>"}}, "13d671e62ee44286a218dd13d43f2037": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6abceb64bd5f495ea6446f138c0ceec9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7091bee89f94485eb3c5cb69c156cf0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6937d3b128744f6c9b6c2b3301bb0b91", "style": 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    </body>
    </html>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 259-280

Understanding the checks' results!
==================================
Whoa! It looks like we have some time leakage!

The ``Conditions`` Summary section showed that the ``Date Train-Test Leakage (overlap)``
check was the only failed check. The ``Additional Outputs`` section helped us understand
that the latest date in the train set belongs to January 2020!

It seems some entries from January 2020 made their way into the train set. We assumed
the ``month`` columns was enough to split the data with (which it would, have all data
was indeed from 2019), but as in real life, things were a bit messy. We'll adjust our
preprocessing real quick, and with methodological errors out of the way we'll get to
checking our model's performance.

it is also worth mentioning that deepchecks found that ``urlLength`` is the only
feature that alone can predict the target with some measure of success. This is
worth investigating!

Adjusting our preprocessing and refitting the model
---------------------------------------------------
Let's just drop any row from 2020 from the raw dataframe and take it all from there

.. GENERATED FROM PYTHON SOURCE LINES 280-284

.. code-block:: default


    df = df[~df['scrape_date'].str.contains('2020')]
    df.shape





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (10896, 25)



.. GENERATED FROM PYTHON SOURCE LINES 285-288

.. code-block:: default


    pipeline = get_url_preprocessor()








.. GENERATED FROM PYTHON SOURCE LINES 289-295

.. code-block:: default


    train_df = pipeline.fit_transform(raw_train_df)
    train_X = train_df.drop('target', axis=1)
    train_y = train_df['target']
    train_X.head(3)






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.. GENERATED FROM PYTHON SOURCE LINES 296-302

.. code-block:: default


    test_df = pipeline.transform(raw_test_df)
    test_X = test_df.drop('target', axis=1)
    test_y = test_df['target']
    test_X.head(3)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
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        .dataframe tbody tr th {
            vertical-align: top;
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        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>urlLength</th>
          <th>numDigits</th>
          <th>numParams</th>
          <th>num_%20</th>
          <th>num_@</th>
          <th>entropy</th>
          <th>hasHttp</th>
          <th>hasHttps</th>
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          <th>dse</th>
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          <th>2019-10-01</th>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 303-306

.. code-block:: default


    logreg.fit(train_X, train_y)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    LogisticRegression(C=0.009, random_state=832)



.. GENERATED FROM PYTHON SOURCE LINES 307-310

.. code-block:: default


    pred_y = logreg.predict(test_X)








.. GENERATED FROM PYTHON SOURCE LINES 311-314

.. code-block:: default


    accuracy_score(test_y, pred_y)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    0.9698972099853157



.. GENERATED FROM PYTHON SOURCE LINES 315-318

Deepchecks' Performance Checks
==============================
Ok! Now that we're back on track lets run some performance checks to see how we did.

.. GENERATED FROM PYTHON SOURCE LINES 318-321

.. code-block:: default


    from deepchecks.tabular.suites import model_evaluation








.. GENERATED FROM PYTHON SOURCE LINES 322-325

.. code-block:: default


    msuite = model_evaluation()








.. GENERATED FROM PYTHON SOURCE LINES 326-330

.. code-block:: default


    ds_train = deepchecks.tabular.Dataset(df=train_X, label=train_y, set_datetime_from_dataframe_index=True, cat_features=[])
    ds_test = deepchecks.tabular.Dataset(df=test_X, label=test_y, set_datetime_from_dataframe_index=True, cat_features=[])





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Dataframe index has duplicate indexes, setting index to [0,1..,n-1].
    Dataframe index has duplicate indexes, setting index to [0,1..,n-1].




.. GENERATED FROM PYTHON SOURCE LINES 331-334

.. code-block:: default


    msuite.run(model=logreg, train_dataset=ds_train, test_dataset=ds_test)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Model Evaluation Suite:
    |           | 0/11 [Time: 00:00]
    Model Evaluation Suite:
    |█          | 1/11 [Time: 00:00, Check=Train Test Performance]
    Model Evaluation Suite:
    |████       | 4/11 [Time: 00:00, Check=Prediction Drift]      
    Model Evaluation Suite:
    |████       | 4/11 [Time: 00:16, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |██████     | 6/11 [Time: 01:12, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |█████████  | 9/11 [Time: 01:12, Check=Unused Features]          
                                                                               

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white-space: pre-wrap;\n}\n#T_d4001_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4001_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4001_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4001_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d4001_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_d4001_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_d4001_row0_col1\" class=\"data row0 col1\" ><a href=\"#SimpleModelComparison_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\">Simple Model Comparison</a></td>\n      <td id=\"T_d4001_row0_col2\" class=\"data row0 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_d4001_row0_col3\" class=\"data row0 col3\" >Found classes with failed metric's gain: {0: {'F1': '2.34%'}, 1: {'F1': '4.65%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_d4001_row1_col0\" class=\"data row1 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_d4001_row1_col1\" class=\"data row1 col1\" ><a href=\"#WeakSegmentsPerformance-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\">Weak Segments Performance - Train Dataset</a></td>\n      <td id=\"T_d4001_row1_col2\" class=\"data row1 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_d4001_row1_col3\" class=\"data row1 col3\" >Found a segment with accuracy score of 0.74 in comparison to an average score of 0.969 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_d4001_row2_col0\" class=\"data row2 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_d4001_row2_col1\" class=\"data row2 col1\" ><a href=\"#WeakSegmentsPerformance-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\">Weak Segments Performance - Test Dataset</a></td>\n      <td id=\"T_d4001_row2_col2\" class=\"data row2 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_d4001_row2_col3\" class=\"data row2 col3\" >Found a segment with accuracy score of 0.68 in comparison to an average score of 0.97 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5211d686f90848e69dad15fc848ca104": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1d9af75d0b5420c9e473102aaf86907": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ec8ef3068bc4830bc4bb28fd20d73e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a4373d7950d7406ba242f4ad02df0053", "style": "IPY_MODEL_28d90ea436534bf89f675c1ed49b01e5", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a4373d7950d7406ba242f4ad02df0053": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "28d90ea436534bf89f675c1ed49b01e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e787883116fa4a0c91d1d6578de47382": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_4965b08a97bb42e5b9c67111eb6dbc7c", "IPY_MODEL_e4a896db457d45668b487e2ce2019ad2", "IPY_MODEL_cf14c71bbffd44b3ac9190148aa0b3da", "IPY_MODEL_99a91838fc9f453eab123b5a4f959143"], "layout": "IPY_MODEL_186c72ea739442158bfc9fd27f242259"}}, "4965b08a97bb42e5b9c67111eb6dbc7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce621637503d4757861f0872d12bdecb", "style": "IPY_MODEL_bd31eac81794466fbdba5653879bcc2d", "value": "<h4 id=\"SimpleModelComparison_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\"><b>Simple Model Comparison</b></h4>"}}, "ce621637503d4757861f0872d12bdecb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bd31eac81794466fbdba5653879bcc2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4a896db457d45668b487e2ce2019ad2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e62b48b7d1874fd08c3072d09f5d96d6", "style": "IPY_MODEL_f033ebf2058e4287a1edfae1a342f56a", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e62b48b7d1874fd08c3072d09f5d96d6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f033ebf2058e4287a1edfae1a342f56a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cf14c71bbffd44b3ac9190148aa0b3da": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a8230799204c4dc696d608b6820086e8", "style": "IPY_MODEL_e4803038419d47c6a16339407f13b658", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6e4c2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e4c2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e4c2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e4c2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e4c2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6e4c2_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6e4c2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_6e4c2_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_6e4c2_row0_col2\" class=\"data row0 col2\" >Found classes with failed metric's gain: {0: {'F1': '2.34%'}, 1: {'F1': '4.65%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a8230799204c4dc696d608b6820086e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4803038419d47c6a16339407f13b658": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "99a91838fc9f453eab123b5a4f959143": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9d60ddf1d344471b9f90eca165c8072f", "IPY_MODEL_0409eef4b97f41d7a8c24d0efbc3a83d", "IPY_MODEL_6a8f9b952bb0476694d4aa369b6a57ac"], "layout": "IPY_MODEL_9cedbda2548146019648a10b1e1473c1"}}, "9d60ddf1d344471b9f90eca165c8072f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ea207a8b947d4951b778e88c40041aa4", "style": "IPY_MODEL_0e8d822d8b46430084b3d8e4bf1b752e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ea207a8b947d4951b778e88c40041aa4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e8d822d8b46430084b3d8e4bf1b752e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0409eef4b97f41d7a8c24d0efbc3a83d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", 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[2]}, "yaxis": "y", "type": "histogram", "uid": "e844fb76-244f-4f74-9b5d-38c6a07499a7"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": 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"IPY_MODEL_b21b527ba0df4ab78c2481981228af6a", "style": "IPY_MODEL_4571e62e459d48b1aaa3a1707e2f61b1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b21b527ba0df4ab78c2481981228af6a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4571e62e459d48b1aaa3a1707e2f61b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec46d9fb66f84a1080bc91791d6273c8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_88a51fab04fe4fb29fc98e81075ed833", "IPY_MODEL_891a33d1cb2e44428828736d63d97cec", "IPY_MODEL_9c5b0d39fbfc40509b3a8830ba10c19b", "IPY_MODEL_4d179498856e4ba3bf0de2995e59e0f0"], "layout": "IPY_MODEL_4fa8141801ed4c349650b5acd604adf5"}}, "88a51fab04fe4fb29fc98e81075ed833": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9a4325536cd468f8a2b835edad54407", "style": "IPY_MODEL_f72fef0a720a45348c8892e46fd38692", "value": "<h4 id=\"WeakSegmentsPerformance-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\"><b>Weak Segments Performance - Train Dataset</b></h4>"}}, "b9a4325536cd468f8a2b835edad54407": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f72fef0a720a45348c8892e46fd38692": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "891a33d1cb2e44428828736d63d97cec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2da71eb345424807bee40508156aa60e", "style": "IPY_MODEL_091b81dd34274f2985db0a088743e23a", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "2da71eb345424807bee40508156aa60e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "091b81dd34274f2985db0a088743e23a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9c5b0d39fbfc40509b3a8830ba10c19b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7288f18f6d8a48de9d34ce2c35be29b7", "style": "IPY_MODEL_9fd91f4c0d1b476a9dd748dad809e443", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d8614_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d8614_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d8614_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d8614_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d8614_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d8614_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_d8614_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_d8614_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_d8614_row0_col2\" class=\"data row0 col2\" >Found a segment with accuracy score of 0.74 in comparison to an average score of 0.969 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7288f18f6d8a48de9d34ce2c35be29b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9fd91f4c0d1b476a9dd748dad809e443": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d179498856e4ba3bf0de2995e59e0f0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1f7157f471364355ac6c25b9526098f2", "IPY_MODEL_49347c53babc4710bdf66572bc0e6178", "IPY_MODEL_9d72514aaa364bae882bb875b184bc9f", "IPY_MODEL_7d503b77c7ac4069b40d7a4862011de8"], "layout": "IPY_MODEL_945647b8b2bf458680f559837ddd5eb5"}}, "1f7157f471364355ac6c25b9526098f2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_730b8acd53c54ce39455073c1d10a486", "style": "IPY_MODEL_093b19d19f4c475aa4c6255b84777fb9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "730b8acd53c54ce39455073c1d10a486": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "093b19d19f4c475aa4c6255b84777fb9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49347c53babc4710bdf66572bc0e6178": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_559e479e0bc444bd88d370603ee2a702", "style": "IPY_MODEL_5b58ae83b78245fa8af5aa8704993bab", "value": "<div>Showcasing intersections of features with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "559e479e0bc444bd88d370603ee2a702": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5b58ae83b78245fa8af5aa8704993bab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9d72514aaa364bae882bb875b184bc9f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4cab3d9010e7469c9590eb49d31aa709", "IPY_MODEL_e6de03c0aa0946ce9b111f6c219080e4"], "layout": "IPY_MODEL_195b6e0643084a88920eae8bd1d40732"}}, "4cab3d9010e7469c9590eb49d31aa709": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_304a38c7b89b400692383ed9e6382fd8", "style": 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{"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Accuracy Score (percent of 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"#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Accuracy Score (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "numLinks"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "18802a1170744acc93f60f86bc4ddd89": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_79a91bb6f4a84c4193c2cbec4c431374", "style": "IPY_MODEL_79f9cbf84b904dc59679977b8a2fca42", "value": "<div>Check ran on 8626 data samples. Accuracy Score on the full data set is 0.97.</div>"}}, "79a91bb6f4a84c4193c2cbec4c431374": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "79f9cbf84b904dc59679977b8a2fca42": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29dd5b2ef31a4f99b252732b94b28c4f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edaa451c80b249018fa10f9376acc10e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "195b6e0643084a88920eae8bd1d40732": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d503b77c7ac4069b40d7a4862011de8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_612f4ec36cbd4781927ec97905f9a7f8", "style": "IPY_MODEL_8d58882793f243c8a82604fdda48aceb", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\" style=\"font-size: 14px;\">Go to top</a>"}}, "612f4ec36cbd4781927ec97905f9a7f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d58882793f243c8a82604fdda48aceb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "945647b8b2bf458680f559837ddd5eb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4fa8141801ed4c349650b5acd604adf5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f374f40401894472bd004e73c45c6d60": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_92443ad78ef2417e89ea0e7f2a3c5a70", "IPY_MODEL_1082f181f461466eb3b56ed99043f553", "IPY_MODEL_6b75f54a42d84ef79b15e6f909f11dde", "IPY_MODEL_5a5734db214b4a99b82c1fad22e4fe6a"], "layout": "IPY_MODEL_b295e3b96e464445aeab7778c804c538"}}, "92443ad78ef2417e89ea0e7f2a3c5a70": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0d4b003ca99141b48869c9ddde93f721", "style": "IPY_MODEL_69ebd194e0ea43c7b96d0991efd515b1", "value": "<h4 id=\"WeakSegmentsPerformance-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\"><b>Weak Segments Performance - Test Dataset</b></h4>"}}, "0d4b003ca99141b48869c9ddde93f721": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69ebd194e0ea43c7b96d0991efd515b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1082f181f461466eb3b56ed99043f553": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9e6e80e55f9146a7ae1cbdee02281303", "style": "IPY_MODEL_b8beb26d135e445e94087c4360579289", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9e6e80e55f9146a7ae1cbdee02281303": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b8beb26d135e445e94087c4360579289": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6b75f54a42d84ef79b15e6f909f11dde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b517065da2254c448a8a3d10b92cb970", "style": "IPY_MODEL_6a982c1929aa4bcc904d6fa1f31b3e88", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_9cf15_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9cf15_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9cf15_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9cf15_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9cf15_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9cf15_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_9cf15_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_9cf15_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_9cf15_row0_col2\" class=\"data row0 col2\" >Found a segment with accuracy score of 0.68 in comparison to an average score of 0.97 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b517065da2254c448a8a3d10b92cb970": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6a982c1929aa4bcc904d6fa1f31b3e88": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5a5734db214b4a99b82c1fad22e4fe6a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c1d7d07809114583ad6115db9af44164", "IPY_MODEL_7a699146f06144b1b57fbafabfd83b58", "IPY_MODEL_ad531fcfa4434a9db07af55bc08f5628", "IPY_MODEL_69b8554a73b94415af7519ce15c17fb8"], "layout": "IPY_MODEL_4d97cef3413044638f146eff0c6c5126"}}, "c1d7d07809114583ad6115db9af44164": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6e406355921c4a0095e282198eebb68a", "style": "IPY_MODEL_4d5427c14caa4cb6b7eea1aeac314bc3", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6e406355921c4a0095e282198eebb68a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d5427c14caa4cb6b7eea1aeac314bc3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a699146f06144b1b57fbafabfd83b58": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0520aa039bab45f4a0ffe334c17207ac", "style": "IPY_MODEL_c13566834a9c4c718d7ca05dfdcb25e7", "value": "<div>Showcasing intersections of features with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "0520aa039bab45f4a0ffe334c17207ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c13566834a9c4c718d7ca05dfdcb25e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad531fcfa4434a9db07af55bc08f5628": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": 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"white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Accuracy Score (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "numLinks"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "b5dfdc5d5cc84abb9a0defbad31453fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d09dba18fe8e4f7583e35b69b7f5cd29", "style": "IPY_MODEL_df01643c6a24450db3b7d86edeb2fd2d", "value": "<div>Check ran on 2724 data samples. Accuracy Score on the full data set is 0.97.</div>"}}, "d09dba18fe8e4f7583e35b69b7f5cd29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df01643c6a24450db3b7d86edeb2fd2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "48ab43b3ae9c4d0ebde50009bb53dc41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "985ab56b074d42b0b1e029dc1ffa7172": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eddb977d73a14cf0a6b49746217b049f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69b8554a73b94415af7519ce15c17fb8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51671c126fda41aa9c6d5541964949f3", "style": "IPY_MODEL_36c803e5909640ef8fee8d9a11af352c", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-7Q4EA\" style=\"font-size: 14px;\">Go to top</a>"}}, "51671c126fda41aa9c6d5541964949f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36c803e5909640ef8fee8d9a11af352c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d97cef3413044638f146eff0c6c5126": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b295e3b96e464445aeab7778c804c538": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "75a3c92bb8c14ff6a3e7f0aaaeb6b5ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "266744052f2646c1abc09dc690d7fda7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e236be411c84c448729923f0a19e2b2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f907f66d18fa4c199cfadaa68e56eaa6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7e245ffafbb74351a35dc9e08878cf72", "IPY_MODEL_969494828e56455cbec72ac117130730"], "layout": "IPY_MODEL_9b7019003f3f4bbdb875d7592bd1b2a0"}}, "7e245ffafbb74351a35dc9e08878cf72": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3005bc60fd84426a0710f34e861ba58", "style": "IPY_MODEL_ec07f45903e8483aa6473bcd711b1167", "value": "<span id=\"summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"></span>"}}, "b3005bc60fd84426a0710f34e861ba58": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec07f45903e8483aa6473bcd711b1167": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "969494828e56455cbec72ac117130730": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_ce2cd35002b74db9a62c182ca019ae0b"], "layout": "IPY_MODEL_a3fa2f107cda4390a9faf1e12acb69f9", "selected_index": null}}, "ce2cd35002b74db9a62c182ca019ae0b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c9b9b8206a294005803996367e7f4b3a", "IPY_MODEL_fafd74e6aac6478e85664b85c4596362", "IPY_MODEL_c225bca25f084a3baa642b93f1336256", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_dde58d76cd454ce8817143f0b811cb6f", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_24247863649a4b289cbdb708bc1f9843", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_e48cbf7cdb3a4cf9a216fc2b7164c487", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_b5fa9b8a3bb944e0b81a50c5f18519d1", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_1ff0a8358081451dbdebd6f9f373d97c", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_72bc7e4ba06f42a4b0e912a5c476e92b", "IPY_MODEL_021aa62995d24b4faf2243f8e854dc31", "IPY_MODEL_b9ce8dd8db1643f98d9327b174d6acc9"], "layout": "IPY_MODEL_11fdd96c498944ee9241a4aa2ec5594b"}}, "c9b9b8206a294005803996367e7f4b3a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7337d7b7350441a9b93dbf78f82e7411", "style": "IPY_MODEL_cbf27a5222f548938175a4138b99a69d", "value": "<style type=\"text/css\">\n#T_2bcd6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2bcd6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2bcd6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2bcd6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2bcd6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2bcd6_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_2bcd6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestPerformance_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">Train Test Performance</a></td>\n      <td id=\"T_2bcd6_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_2bcd6_row0_col3\" class=\"data row0 col3\" >Found max degradation of 0% for metric Recall and class 0.</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row1_col1\" class=\"data row1 col1\" ><a href=\"#ROCReport-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">ROC Report - Train Dataset</a></td>\n      <td id=\"T_2bcd6_row1_col2\" class=\"data row1 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_2bcd6_row1_col3\" class=\"data row1 col3\" >All classes passed, minimum AUC found is 0.9 for class 1</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row2_col1\" class=\"data row2 col1\" ><a href=\"#ROCReport-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">ROC Report - Test Dataset</a></td>\n      <td id=\"T_2bcd6_row2_col2\" class=\"data row2 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_2bcd6_row2_col3\" class=\"data row2 col3\" >All classes passed, minimum AUC found is 0.89 for class 1</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row3_col1\" class=\"data row3 col1\" ><a href=\"#PredictionDrift_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">Prediction Drift</a></td>\n      <td id=\"T_2bcd6_row3_col2\" class=\"data row3 col2\" >Prediction drift score < 0.15</td>\n      <td id=\"T_2bcd6_row3_col3\" class=\"data row3 col3\" >Found model prediction Kolmogorov-Smirnov drift score of 0.04</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row4_col1\" class=\"data row4 col1\" ><a href=\"#UnusedFeatures-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">Unused Features - Train Dataset</a></td>\n      <td id=\"T_2bcd6_row4_col2\" class=\"data row4 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_2bcd6_row4_col3\" class=\"data row4 col3\" >Found 2 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row5_col1\" class=\"data row5 col1\" ><a href=\"#UnusedFeatures-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">Unused Features - Test Dataset</a></td>\n      <td id=\"T_2bcd6_row5_col2\" class=\"data row5 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_2bcd6_row5_col3\" class=\"data row5 col3\" >Found 1 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_2bcd6_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_2bcd6_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 2.28e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_2bcd6_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_2bcd6_row7_col1\" class=\"data row7 col1\" ><a href=\"#ModelInferenceTime-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_2bcd6_row7_col2\" class=\"data row7 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_2bcd6_row7_col3\" class=\"data row7 col3\" >Found average inference time (seconds): 4.25e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7337d7b7350441a9b93dbf78f82e7411": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cbf27a5222f548938175a4138b99a69d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fafd74e6aac6478e85664b85c4596362": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a4a9a442adcf47edae41be6c84d043ee", "style": "IPY_MODEL_91b166f2010f44f0819e66460ab12989", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a4a9a442adcf47edae41be6c84d043ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "91b166f2010f44f0819e66460ab12989": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c225bca25f084a3baa642b93f1336256": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_665df091db21498da80a405c76350a2f", "IPY_MODEL_95d3b8383c084e9ea91c840ddf44230a", "IPY_MODEL_8b6b1474d01447e28a9fbd79ec12e267", "IPY_MODEL_4204161a7a7b49e4bcd0779af016a342"], "layout": "IPY_MODEL_542886ac30a2420cb67a0e8709724c52"}}, "665df091db21498da80a405c76350a2f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21e28fa613c44523b4143ab72c02051e", "style": "IPY_MODEL_80cd1765a7fe483ab12cc8fc86833c7d", "value": "<h4 id=\"TrainTestPerformance_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>Train Test Performance</b></h4>"}}, "21e28fa613c44523b4143ab72c02051e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80cd1765a7fe483ab12cc8fc86833c7d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "95d3b8383c084e9ea91c840ddf44230a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7876d3cc02114850a99731bcb3cc564c", "style": "IPY_MODEL_b6da2f7c738145218ac5037527444b8a", "value": "<p>Summarize given model performance on the train and test datasets based on selected scorers. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7876d3cc02114850a99731bcb3cc564c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6da2f7c738145218ac5037527444b8a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8b6b1474d01447e28a9fbd79ec12e267": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_332893ce445a437d887636fabb0f243f", "style": "IPY_MODEL_091529d4b19b452cb0b450a35357028f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_cf493_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cf493_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cf493_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cf493_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cf493_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_cf493_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_cf493_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cf493_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_cf493_row0_col2\" class=\"data row0 col2\" >Found max degradation of 0% for metric Recall and class 0.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "332893ce445a437d887636fabb0f243f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "091529d4b19b452cb0b450a35357028f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4204161a7a7b49e4bcd0779af016a342": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bbaa789ff8094206a0f0710112648063", "IPY_MODEL_491de244a2fc43b6a928388a17ce037b", "IPY_MODEL_ae1f2b12a58945e9beaf9b2c397c41d5"], "layout": "IPY_MODEL_89a6daff243b4035b59328808a9369db"}}, "bbaa789ff8094206a0f0710112648063": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a68cdb7d9474c5ebfca12132f8a455f", "style": "IPY_MODEL_030617813d274197a166ab31ca032aa4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3a68cdb7d9474c5ebfca12132f8a455f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "030617813d274197a166ab31ca032aa4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "491de244a2fc43b6a928388a17ce037b": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=F1<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "485e9eaf-a605-4ce4-ad07-356e5cf97cf1"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": [0, 1], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "histogram", "uid": "72c1e86f-a63a-4937-9a44-45fa1c5a9cc3"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": [0, 1], "xaxis": "x3", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y3", "type": "histogram", "uid": "aa5d0abd-959d-47f5-82a9-431c35c10f89"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=F1<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "17e38d62-ecc1-4184-9c9b-6e8db4244ce0"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Precision<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": [0, 1], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "histogram", "uid": "b4f069d7-9f1b-4d6d-acc0-b3445eae52ba"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Recall<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": [0, 1], "xaxis": "x3", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y3", "type": "histogram", "uid": "4bcc1912-b0aa-4ef3-9319-edcbcd19771e"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.15, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Precision", "x": 0.49999999999999994, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Recall", "x": 0.85, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Class", "x": -0.1, "xref": "paper", "y": -0.1, "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Dataset"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, 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[0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, 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"@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dde58d76cd454ce8817143f0b811cb6f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_29bc57c1ec0748d7a9d2a6c85f9e32a2", "IPY_MODEL_2b32bbda2e6a4cffa8d632ae1bd4f2d9", "IPY_MODEL_9d492b0a96b0473a8e46314c4136b7c5", "IPY_MODEL_7ae0a9568cca4c4c8fc8f890a264f391"], "layout": "IPY_MODEL_c3b0a8b7363a41c1af2022fd84ebb8cf"}}, "29bc57c1ec0748d7a9d2a6c85f9e32a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fad64f6d61e7476f995d15cd18f710c6", "style": "IPY_MODEL_31e2d3de1e5d483c84366895cf33e541", "value": "<h4 id=\"ROCReport-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>ROC Report - Train Dataset</b></h4>"}}, "fad64f6d61e7476f995d15cd18f710c6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "31e2d3de1e5d483c84366895cf33e541": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2b32bbda2e6a4cffa8d632ae1bd4f2d9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a4ffa3866a4445baf3065fa9e6084a9", "style": "IPY_MODEL_d9b257210a41410380b840072e9cb2c0", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8a4ffa3866a4445baf3065fa9e6084a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d9b257210a41410380b840072e9cb2c0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9d492b0a96b0473a8e46314c4136b7c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e23b79ea26484fd29033f8759a0f473b", "style": "IPY_MODEL_9209305ad080494aac3982387e265cac", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_54823_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_54823_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_54823_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_54823_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_54823_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_54823_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_54823_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_54823_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_54823_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 0.9 for class 1</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e23b79ea26484fd29033f8759a0f473b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9209305ad080494aac3982387e265cac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7ae0a9568cca4c4c8fc8f890a264f391": {"model_name": "VBoxModel", "model_module": 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"b793d763acac46758610667e912c9645": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_667976fdd07a42e7a53014ecefc89803", "style": "IPY_MODEL_7116c9fda37d4916b37e43adfa4c1a38", "value": "<div>The marked points are the optimal probability threshold cut-off points to predict said\n            class. In plain terms, it is optimal to set the prediction rule such that if for some class the predicted\n            probability is above the threshold of that class, then the prediction should be that class.\n            They optimal thresholds are determined using Youden's index defined as sensitivity + specificity - 1.</div>"}}, "667976fdd07a42e7a53014ecefc89803": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7116c9fda37d4916b37e43adfa4c1a38": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "91123df5571640819766bdd8b67aced5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9db21477839b4a07b30009cfcd1d2cf9", "style": "IPY_MODEL_c8e8c5023000435095caa745560f3fc4", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "9db21477839b4a07b30009cfcd1d2cf9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c8e8c5023000435095caa745560f3fc4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cf46ee1c4e7c4d2b8831690633af6047": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3b0a8b7363a41c1af2022fd84ebb8cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "24247863649a4b289cbdb708bc1f9843": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_25e577c245c044cc9fc30aabb56a85f1", "IPY_MODEL_8e56db60b8194b4c814ad55296571a51", "IPY_MODEL_6ee3fbcb8b744ff280ed5635a9ab843a", "IPY_MODEL_4cf74bac0611475bbbd838f32db46fe9"], "layout": "IPY_MODEL_2b117a5e7214441fb86a452acac22f2a"}}, "25e577c245c044cc9fc30aabb56a85f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9ede9fffa8504c1e972f1d0f4e5f3c20", "style": "IPY_MODEL_a2938aedc12d4ff28b8804b01f9486ce", "value": "<h4 id=\"ROCReport-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>ROC Report - Test Dataset</b></h4>"}}, "9ede9fffa8504c1e972f1d0f4e5f3c20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2938aedc12d4ff28b8804b01f9486ce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e56db60b8194b4c814ad55296571a51": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9603c1e1634448bca7f6a157ec88bc19", "style": "IPY_MODEL_5e66216b36ee46dfa1bd0052c19af29e", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9603c1e1634448bca7f6a157ec88bc19": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e66216b36ee46dfa1bd0052c19af29e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6ee3fbcb8b744ff280ed5635a9ab843a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3676acf663fc442cb656c79cc39a30e3", "style": "IPY_MODEL_13c5092c89ba4a10a43add105683c2af", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a1ae9_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a1ae9_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a1ae9_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a1ae9_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a1ae9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a1ae9_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a1ae9_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a1ae9_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_a1ae9_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 0.89 for class 1</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3676acf663fc442cb656c79cc39a30e3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13c5092c89ba4a10a43add105683c2af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4cf74bac0611475bbbd838f32db46fe9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0a6962ff9acb45d6815375f112497e5d", "IPY_MODEL_d6716adca37d45aaa5752cd64cc2c4b0", "IPY_MODEL_0af2878750f4475c99b732cddd328422", "IPY_MODEL_31690752faab4190ad87a2650e0fc6a8"], "layout": "IPY_MODEL_c0d0e739fd2248808d8584bddca4298f"}}, "0a6962ff9acb45d6815375f112497e5d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_309de8dd968d4578b10b15ea7f0bd026", "style": "IPY_MODEL_b362d3d41eca4d5bb09751b2a685605d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "309de8dd968d4578b10b15ea7f0bd026": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b362d3d41eca4d5bb09751b2a685605d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d6716adca37d45aaa5752cd64cc2c4b0": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "Class 1 (auc = 0.89)", "x": {"dtype": "float64", "shape": 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"0af2878750f4475c99b732cddd328422": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a4e014f501804a67af964a9011426a65", "style": "IPY_MODEL_33a2976e487444818926f0c16f8d4f08", "value": "<div>The marked points are the optimal probability threshold cut-off points to predict said\n            class. In plain terms, it is optimal to set the prediction rule such that if for some class the predicted\n            probability is above the threshold of that class, then the prediction should be that class.\n            They optimal thresholds are determined using Youden's index defined as sensitivity + specificity - 1.</div>"}}, "a4e014f501804a67af964a9011426a65": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33a2976e487444818926f0c16f8d4f08": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "31690752faab4190ad87a2650e0fc6a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_62a8227de6074d35b2980c1f8bcb60bc", "style": "IPY_MODEL_9ab37360a5794b38b662085694736e5e", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "62a8227de6074d35b2980c1f8bcb60bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ab37360a5794b38b662085694736e5e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c0d0e739fd2248808d8584bddca4298f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b117a5e7214441fb86a452acac22f2a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e48cbf7cdb3a4cf9a216fc2b7164c487": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_3aa351c71cde4de288ae9239d66105ed", "IPY_MODEL_20089018fd4f4556b69441594e1fd5f7", "IPY_MODEL_89d6fab5da474d5484114f7217e2a636", "IPY_MODEL_7d893418cbb1486cbcc36051e3624cfd"], "layout": "IPY_MODEL_feb15142fa50407298121c05b9cfe5a9"}}, "3aa351c71cde4de288ae9239d66105ed": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2662da5e7f884b708c56e3eec1daf062", "style": "IPY_MODEL_05d9da9fcbd64074b0e091a25efb057e", "value": "<h4 id=\"PredictionDrift_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>Prediction Drift</b></h4>"}}, "2662da5e7f884b708c56e3eec1daf062": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "05d9da9fcbd64074b0e091a25efb057e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "20089018fd4f4556b69441594e1fd5f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1131834190c34fbcad32be3a4e4b018c", "style": "IPY_MODEL_234bce8759aa46cca80b341bc7a1c158", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1131834190c34fbcad32be3a4e4b018c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "234bce8759aa46cca80b341bc7a1c158": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "89d6fab5da474d5484114f7217e2a636": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_560ee6b4f23c443b86f41808f050e3ee", "style": "IPY_MODEL_5d7726360b474eb0be37e67eaa1b5a9b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_47615_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47615_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47615_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47615_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47615_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_47615_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_47615_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_47615_row0_col1\" class=\"data row0 col1\" >Prediction drift score < 0.15</td>\n      <td id=\"T_47615_row0_col2\" class=\"data row0 col2\" >Found model prediction Kolmogorov-Smirnov drift score of 0.04</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "560ee6b4f23c443b86f41808f050e3ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d7726360b474eb0be37e67eaa1b5a9b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d893418cbb1486cbcc36051e3624cfd": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1eea917e1e834e8cb15dbb14c36c2d85", "IPY_MODEL_279a673783fb43acad222689e6517ffb", "IPY_MODEL_22a8c943fbe24243b0af1e18a7874bb7", "IPY_MODEL_7d0ef2fe774646df881fc427ab22bd3d", "IPY_MODEL_adba763046a34bceb222a303bf2a7cf2"], "layout": "IPY_MODEL_cbc75915296b495ab35524524ac9608d"}}, "1eea917e1e834e8cb15dbb14c36c2d85": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07e432fd698c4e7ca4df9f2f09e9386c", "style": "IPY_MODEL_54a7f63226fd4caea2c5f0cb2c1a402d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "07e432fd698c4e7ca4df9f2f09e9386c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54a7f63226fd4caea2c5f0cb2c1a402d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "279a673783fb43acad222689e6517ffb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a03aa5154794f5db19767406becefab", "style": "IPY_MODEL_dc8f5dc35da84c46a6cfbfed0f437f94", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the predicted\n                class probabilities.\n            </span></div>"}}, "5a03aa5154794f5db19767406becefab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc8f5dc35da84c46a6cfbfed0f437f94": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22a8c943fbe24243b0af1e18a7874bb7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c218e20154f04c80b581fabd4558b7e1", "style": "IPY_MODEL_282d1d6673fd4d76b054e6da7f1fead4", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "c218e20154f04c80b581fabd4558b7e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "282d1d6673fd4d76b054e6da7f1fead4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d0ef2fe774646df881fc427ab22bd3d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.041792255970322295], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "4383a52a-403b-4ffa-8c9d-0ae718a91ae4"}, {"fill": "tozeroy", "line": {"color": "#00008b", "shape": "linear"}, "name": "Train Dataset", "x": 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"adba763046a34bceb222a303bf2a7cf2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9972129ea61a4e5ab309724f7ac70c6b", "style": "IPY_MODEL_d8788fc266674c7a980bfb13fcbd3cda", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "9972129ea61a4e5ab309724f7ac70c6b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8788fc266674c7a980bfb13fcbd3cda": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbc75915296b495ab35524524ac9608d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "feb15142fa50407298121c05b9cfe5a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5fa9b8a3bb944e0b81a50c5f18519d1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_7ff7e99d558f49a3a720079b7c77bf76", "IPY_MODEL_3389ae7be4a04e2ca8a8211b21335033", "IPY_MODEL_a7a3a24d63954bddb79a498d9619a7e5", "IPY_MODEL_617a4c880f35469e9b66d7529ad90579"], "layout": "IPY_MODEL_c918317cbd754631a2e2605b7bc564ed"}}, "7ff7e99d558f49a3a720079b7c77bf76": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3dbc40a300d14f92ba999aa765939c2a", "style": "IPY_MODEL_4baaa0cf52d74019b6b6e8c6d5b8eee3", "value": "<h4 id=\"UnusedFeatures-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>Unused Features - Train Dataset</b></h4>"}}, "3dbc40a300d14f92ba999aa765939c2a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4baaa0cf52d74019b6b6e8c6d5b8eee3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3389ae7be4a04e2ca8a8211b21335033": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_acfd809321524eccbb5cf2550fc081b3", "style": "IPY_MODEL_b43c2ea7522e4897942eb11d1020bbaf", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "acfd809321524eccbb5cf2550fc081b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b43c2ea7522e4897942eb11d1020bbaf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7a3a24d63954bddb79a498d9619a7e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_295cf9e0d0294f6590453c4f7bf60a3f", "style": "IPY_MODEL_28e15772efe94b04ba25e3f22ad99f71", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_9324a_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9324a_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9324a_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9324a_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9324a_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9324a_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_9324a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_9324a_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_9324a_row0_col2\" class=\"data row0 col2\" >Found 2 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "295cf9e0d0294f6590453c4f7bf60a3f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "28e15772efe94b04ba25e3f22ad99f71": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "617a4c880f35469e9b66d7529ad90579": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6e91d62ca5ab46c589131ea05d7fe8d4", "IPY_MODEL_78458fc49c284f3f84b598fcbb294ae1", "IPY_MODEL_0ee3c7e9576b4fbe9b29c12dd2a1cbc8", "IPY_MODEL_f5a370ca1c6749059a9490055a1b83c6"], "layout": "IPY_MODEL_a7409fadf17c4bd5b1392d34277aab30"}}, "6e91d62ca5ab46c589131ea05d7fe8d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3247ee4786904f788190cb729aee050f", "style": "IPY_MODEL_cd79ea1dd6c74e91bb66e34d60b9234b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3247ee4786904f788190cb729aee050f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd79ea1dd6c74e91bb66e34d60b9234b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "78458fc49c284f3f84b598fcbb294ae1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2be530546f8841aca035bed69bb15b53", "style": "IPY_MODEL_6bbcff921e9f4fc986f208d2d2b9bfa2", "value": "<div>Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters</div>"}}, "2be530546f8841aca035bed69bb15b53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6bbcff921e9f4fc986f208d2d2b9bfa2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ee3c7e9576b4fbe9b29c12dd2a1cbc8": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "indianred"}, "name": "Feature Importance %", "orientation": "h", "x": {"dtype": "float64", "shape": [7]}, "y": ["bscr", "urlLength", "numParams", "ext_net", "numLinks", "ext_info", "num_%20"], "type": "bar", "uid": "9a4909ba-94d1-448c-8dad-0e498a00fe67"}, {"marker": {"color": "lightsalmon"}, "name": "Feature Variance %", "orientation": "h", "x": {"dtype": "float64", "shape": [7]}, "y": ["bscr", "urlLength", "numParams", "ext_net", "numLinks", "ext_info", "num_%20"], "type": "bar", "uid": "bc5aed18-e39f-4f61-bd74-2ade0c4768d3"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Last shown significant feature", "x": 1, "xanchor": "right", "xref": "x domain", "y": 4.5, "yanchor": "bottom", "yref": "y"}], "height": 500, "shapes": [{"line": {"color": "green", "dash": "dash", "width": 2}, "type": "line", "x0": 0, "x1": 1, "xref": "x domain", "y0": 4.5, "y1": 4.5, "yref": "y"}], "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": 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"scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, 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"#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Unused features compared to top important features"}, "yaxis": {"autorange": "reversed"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": "E8pY48MEJkCTHqiMs6YlQHmLt9uqwSNATN6U/CP9H0BIEQFlCfgXQC/gdMiEet4/5+s4sIk6yj8="}, {"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "hHZgxWTLE0BG+cUNBu0IQP7E3ZCA0hBAL8ROOFEqEEAqPV4/t1oSQN9fWAB8Ag1AGRfq87NdDEA="}]}, "f5a370ca1c6749059a9490055a1b83c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_056305083ee5447296e1504f8648b18f", "style": "IPY_MODEL_599453d81742415a8e8d4596b19effae", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "056305083ee5447296e1504f8648b18f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "599453d81742415a8e8d4596b19effae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7409fadf17c4bd5b1392d34277aab30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c918317cbd754631a2e2605b7bc564ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1ff0a8358081451dbdebd6f9f373d97c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_566eeb8c89b346dc85c53ad220e5d5c7", "IPY_MODEL_eab50e1623864314b6885c2eb41f5162", "IPY_MODEL_4d97ba0c6536419cacf664c6fa04ec56", "IPY_MODEL_cd9ad67f63c043dda68d65687ec1d19f"], "layout": "IPY_MODEL_8f67c126c33d4726b00753d88b224801"}}, "566eeb8c89b346dc85c53ad220e5d5c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7553d5f072884cc6b4be4e51bba7ed8d", "style": "IPY_MODEL_7db2e52e383549c88137a0904ec41231", "value": "<h4 id=\"UnusedFeatures-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>Unused Features - Test Dataset</b></h4>"}}, "7553d5f072884cc6b4be4e51bba7ed8d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7db2e52e383549c88137a0904ec41231": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eab50e1623864314b6885c2eb41f5162": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_94c1e525952347ff8507ffa7c3ba96cf", "style": "IPY_MODEL_d080c254780c46aea0ecb9f8f1c77c6e", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "94c1e525952347ff8507ffa7c3ba96cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d080c254780c46aea0ecb9f8f1c77c6e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d97ba0c6536419cacf664c6fa04ec56": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82c6bd5739844632a2066ce94517a564", "style": "IPY_MODEL_541fb92a032043bcb25a528e79e1c558", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_38fce_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_38fce_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_38fce_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_38fce_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_38fce_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_38fce_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_38fce_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_38fce_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_38fce_row0_col2\" class=\"data row0 col2\" >Found 1 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "82c6bd5739844632a2066ce94517a564": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "541fb92a032043bcb25a528e79e1c558": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cd9ad67f63c043dda68d65687ec1d19f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b1292ce41d5d46a589a3a7b61c57d641", "IPY_MODEL_511a4932a9e140b192e92f80e69e38e5", "IPY_MODEL_431cd14dbeb94642995ae721bd3d4b52", "IPY_MODEL_6f71cc100c2d4b93b581b007f229da7e"], "layout": "IPY_MODEL_bdd0ee3c225440939910ffeda0c8039c"}}, "b1292ce41d5d46a589a3a7b61c57d641": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c4e5b701231b42d98c1fb9e92135c1a6", "style": "IPY_MODEL_660f30ff71e7482593a98bc21fd5b247", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c4e5b701231b42d98c1fb9e92135c1a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "660f30ff71e7482593a98bc21fd5b247": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "511a4932a9e140b192e92f80e69e38e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0aede1b8d82d47b591284370445bf44d", "style": "IPY_MODEL_4ae3714b49ef4fa08a633b74fefd4303", "value": "<div>Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters</div>"}}, "0aede1b8d82d47b591284370445bf44d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ae3714b49ef4fa08a633b74fefd4303": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "431cd14dbeb94642995ae721bd3d4b52": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "indianred"}, "name": "Feature Importance %", "orientation": "h", "x": {"dtype": "float64", "shape": [6]}, "y": ["bscr", "urlLength", "numParams", "ext_net", "numLinks", "ext_info"], "type": "bar", "uid": "a486b560-082d-4982-b600-68cfb6a49e95"}, {"marker": {"color": "lightsalmon"}, "name": "Feature Variance %", "orientation": "h", "x": {"dtype": "float64", "shape": [6]}, "y": ["bscr", "urlLength", "numParams", "ext_net", "numLinks", "ext_info"], "type": "bar", "uid": "5924bbd5-f148-414d-b72a-8c5041918c8b"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Unused features compared to top important features"}, "yaxis": {"autorange": "reversed"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": "E8pY48MEJkCTHqiMs6YlQHmLt9uqwSNATN6U/CP9H0BIEQFlCfgXQC/gdMiEet4/"}, {"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "1gMw5voSEkCNszhrU7QPQLGCFR6OlxRAsPAlyMv8CUB/zb1R6T0JQKv+Oco52hJA"}]}, "6f71cc100c2d4b93b581b007f229da7e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c2e7f658697244b289835f5e63a14967", "style": "IPY_MODEL_72aa21e72a73454aa985b642ff96de8d", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "c2e7f658697244b289835f5e63a14967": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72aa21e72a73454aa985b642ff96de8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bdd0ee3c225440939910ffeda0c8039c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f67c126c33d4726b00753d88b224801": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72bc7e4ba06f42a4b0e912a5c476e92b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_86a99fd1d80740e3b10e8473342e8efe", "IPY_MODEL_dc2af3bbdb9d48bbbd48d430188ccccb", "IPY_MODEL_702c17e6a03b42c3b1684991b8bd38b5", "IPY_MODEL_298d8731521e446e9e2485bc1b60c446"], "layout": "IPY_MODEL_b7c76d1ad79447a988bad3d561849c88"}}, "86a99fd1d80740e3b10e8473342e8efe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b77e35bdab114155830105c9f1e1eeda", "style": "IPY_MODEL_f4538a5c575043deab9f2eec463ed1c9", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "b77e35bdab114155830105c9f1e1eeda": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f4538a5c575043deab9f2eec463ed1c9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dc2af3bbdb9d48bbbd48d430188ccccb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c996b229d09d4356906e02dcb7a2d708", "style": "IPY_MODEL_96db2fefd1f7439ba072c8190bd62440", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c996b229d09d4356906e02dcb7a2d708": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "96db2fefd1f7439ba072c8190bd62440": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "702c17e6a03b42c3b1684991b8bd38b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_af7a79f3a04d46459fdb33665c767ad7", "style": "IPY_MODEL_59e686953ce34a8295a4ad3f1b2530ef", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8a55b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a55b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a55b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a55b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8a55b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8a55b_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_8a55b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8a55b_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_8a55b_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 2.28e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "af7a79f3a04d46459fdb33665c767ad7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59e686953ce34a8295a4ad3f1b2530ef": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "298d8731521e446e9e2485bc1b60c446": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4db3097cc25f4609aa550b490b56936f", "IPY_MODEL_7a568b8990ab4af79e920ff493bcae26", "IPY_MODEL_b44de7adb4b0470da5bacc81e7b7145a", "IPY_MODEL_0591e1da14fb430a8a438eaaa06357e8"], "layout": "IPY_MODEL_777eae0ba3854783916e40c8d093826c"}}, "4db3097cc25f4609aa550b490b56936f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_97a60d48e0aa4fa585952238d01e91ff", "style": "IPY_MODEL_12eb38293d7d4885a254a933edb1fa8d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "97a60d48e0aa4fa585952238d01e91ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "12eb38293d7d4885a254a933edb1fa8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a568b8990ab4af79e920ff493bcae26": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_88ce4cdd3b414214b51855fdc7483601", "style": "IPY_MODEL_ca46374b621242a9b7cb51735ff74cc8", "value": "<div>Average model inference time for one sample (in seconds): 2.28e-06</div>"}}, "88ce4cdd3b414214b51855fdc7483601": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ca46374b621242a9b7cb51735ff74cc8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b44de7adb4b0470da5bacc81e7b7145a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2cb005bf5384428980ca12e3d7ada465", "style": "IPY_MODEL_ae20fc75cb9144d186d1652824c2ddbb", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 8626. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "2cb005bf5384428980ca12e3d7ada465": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae20fc75cb9144d186d1652824c2ddbb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0591e1da14fb430a8a438eaaa06357e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_36c3e52f355542808d89d5c9b0a030e7", "style": "IPY_MODEL_ee09d2ad77d645d6aecc1a55bf9bd340", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "36c3e52f355542808d89d5c9b0a030e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee09d2ad77d645d6aecc1a55bf9bd340": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "777eae0ba3854783916e40c8d093826c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7c76d1ad79447a988bad3d561849c88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b9ce8dd8db1643f98d9327b174d6acc9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_d53c2a94ed5b47b2be978407e2e43c16", "IPY_MODEL_4cb824e9fd10483ca512e52416aa5216", "IPY_MODEL_ced13b9d410b4cd28a33bdb347e42806", "IPY_MODEL_ef742672a7d94f4da347419e7de3425f"], "layout": "IPY_MODEL_be069bca52b34524877b984471f75aef"}}, "d53c2a94ed5b47b2be978407e2e43c16": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d4fe79779d5948a99be079443498256f", "style": "IPY_MODEL_8c09e9139ea04f1593ba932db0221732", "value": "<h4 id=\"ModelInferenceTime-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "d4fe79779d5948a99be079443498256f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8c09e9139ea04f1593ba932db0221732": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4cb824e9fd10483ca512e52416aa5216": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b276f00f41946578702ed08d640782a", "style": "IPY_MODEL_1784af0d1e4548ae8364d13982a647e5", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6b276f00f41946578702ed08d640782a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1784af0d1e4548ae8364d13982a647e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ced13b9d410b4cd28a33bdb347e42806": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21a2807fab064ae2845f7fa803458b15", "style": "IPY_MODEL_3e2fa72c6bae49bf8520999322e312e5", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_1e058_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1e058_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1e058_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1e058_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1e058_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1e058_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_1e058_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1e058_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_1e058_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 4.25e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "21a2807fab064ae2845f7fa803458b15": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e2fa72c6bae49bf8520999322e312e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef742672a7d94f4da347419e7de3425f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5aa507829f0a41d7a648db4e726010c2", "IPY_MODEL_bb38d74adc9740d3a1b607e6022b322c", "IPY_MODEL_786d6e1630ed4e45a34ff7e6b5e47039", "IPY_MODEL_650c4f15c48043038d2d6b8ba9c757a8"], "layout": "IPY_MODEL_551c61164f95485989ccc4a1b984c657"}}, "5aa507829f0a41d7a648db4e726010c2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3451329f76f6483c9e7a42c8f14bd13e", "style": "IPY_MODEL_9635a6a18c8d40a7b95f11bd7dafd1f0", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3451329f76f6483c9e7a42c8f14bd13e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9635a6a18c8d40a7b95f11bd7dafd1f0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb38d74adc9740d3a1b607e6022b322c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_45e73e640d38457bb3e288298c9a1280", "style": "IPY_MODEL_9260362ab8344748b2b736492b54efec", "value": "<div>Average model inference time for one sample (in seconds): 4.25e-06</div>"}}, "45e73e640d38457bb3e288298c9a1280": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9260362ab8344748b2b736492b54efec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "786d6e1630ed4e45a34ff7e6b5e47039": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_39934824630b4b4797680337b240f9ed", "style": "IPY_MODEL_fc2c4743283949b59697a3c7600c53f7", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 2724. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "39934824630b4b4797680337b240f9ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc2c4743283949b59697a3c7600c53f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "650c4f15c48043038d2d6b8ba9c757a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_78e2e8de617949d58fddee4ce8362284", "style": "IPY_MODEL_dffd2d042d4641d199c96eb3587d68a8", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-U9YTN\" style=\"font-size: 14px;\">Go to top</a>"}}, "78e2e8de617949d58fddee4ce8362284": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dffd2d042d4641d199c96eb3587d68a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "551c61164f95485989ccc4a1b984c657": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be069bca52b34524877b984471f75aef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "11fdd96c498944ee9241a4aa2ec5594b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a3fa2f107cda4390a9faf1e12acb69f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b7019003f3f4bbdb875d7592bd1b2a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5099b87d4b654d038cf786ab3fa66a2c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_065b354814f442489320706464d9c85d", "IPY_MODEL_b6ca9441d1c24a0e8e0e27db857dafe3"], "layout": "IPY_MODEL_8b99f484c4c34d7eaea5264a7b771d02"}}, "065b354814f442489320706464d9c85d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c0f8cb61dda4c488cedab8d803a5357", "style": "IPY_MODEL_72a6602f838e46809adb6a30feffb2ff", "value": "<span id=\"summary_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\"></span>"}}, "5c0f8cb61dda4c488cedab8d803a5357": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72a6602f838e46809adb6a30feffb2ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6ca9441d1c24a0e8e0e27db857dafe3": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_fd1797097cbc42108d5d2a3c1b48d399"], "layout": "IPY_MODEL_1508b75702cc42ef829e796341678e26", "selected_index": null}}, "fd1797097cbc42108d5d2a3c1b48d399": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_12c1683010f7423e85d2f7ec4ef34c87", "IPY_MODEL_530a185aaff148f4b33395e8a21fdfc1", "IPY_MODEL_5efa798949444711b168ca80224f1917", "IPY_MODEL_e04d622b39f34446883dfafa473df20b", "IPY_MODEL_3a8fe994c51441dbacfa32115e70e9f7", "IPY_MODEL_e04d622b39f34446883dfafa473df20b", "IPY_MODEL_039eacd6cbbf419e94abb2908ede97e9", "IPY_MODEL_e04d622b39f34446883dfafa473df20b", "IPY_MODEL_b82c7b97240446709363d0c7b82311a9"], "layout": "IPY_MODEL_35b11e403bf443e1aaca112ef9898f59"}}, "12c1683010f7423e85d2f7ec4ef34c87": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d694b81cb25b44a39a3695f581d01efa", "style": "IPY_MODEL_b4ead1f52be2492197885925f674985d", "value": "<style type=\"text/css\">\n#T_601c1_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_601c1_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_601c1_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_601c1_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_601c1_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_601c1_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_601c1_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrixReport-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\">Confusion Matrix Report - Train Dataset</a></td>\n      <td id=\"T_601c1_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_601c1_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrixReport-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\">Confusion Matrix Report - Test Dataset</a></td>\n      <td id=\"T_601c1_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_601c1_row2_col0\" class=\"data row2 col0\" ><a href=\"#CalibrationMetric-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\">Calibration Metric - Train Dataset</a></td>\n      <td id=\"T_601c1_row2_col1\" class=\"data row2 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_601c1_row3_col0\" class=\"data row3 col0\" ><a href=\"#CalibrationMetric-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\">Calibration Metric - Test Dataset</a></td>\n      <td id=\"T_601c1_row3_col1\" class=\"data row3 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d694b81cb25b44a39a3695f581d01efa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4ead1f52be2492197885925f674985d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "530a185aaff148f4b33395e8a21fdfc1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9dc03f8bfeb14b7187ab77206ed0c564", "style": "IPY_MODEL_d438cd845849493bb3e6247943f724c9", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "9dc03f8bfeb14b7187ab77206ed0c564": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d438cd845849493bb3e6247943f724c9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5efa798949444711b168ca80224f1917": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_7fc7de4e01774e2f8d719a199aa7c6c5", "IPY_MODEL_84d3121bce284bd182959f8ae57e39f9", "IPY_MODEL_3b7811c438354eb29b7a5d4114dfb087"], "layout": "IPY_MODEL_bf0319dc4419452bb07ffd8bc687d0af"}}, "7fc7de4e01774e2f8d719a199aa7c6c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5cfccf6ba4614feab3ad4be620e12c02", "style": "IPY_MODEL_e3078872482b43179120fa0037768853", "value": "<h4 id=\"ConfusionMatrixReport-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\"><b>Confusion Matrix Report - Train Dataset</b></h4>"}}, "5cfccf6ba4614feab3ad4be620e12c02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3078872482b43179120fa0037768853": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "84d3121bce284bd182959f8ae57e39f9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_476559b461e6426ea86edaefe41a0983", "style": "IPY_MODEL_a71b402715334fcd8de1f5bfaa04c24a", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "476559b461e6426ea86edaefe41a0983": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a71b402715334fcd8de1f5bfaa04c24a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b7811c438354eb29b7a5d4114dfb087": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_852dfc55a3c44a9d886d2a54cdfa40d6", "IPY_MODEL_8bc611a4147146ae99936546e8aa377d", "IPY_MODEL_b3db0933b2b642fd9efc7cba16617873", "IPY_MODEL_6968f7ef1e284405805a241c051ef626"], "layout": "IPY_MODEL_2c7eca8021cc4078a6e06b4049aff56f"}}, "852dfc55a3c44a9d886d2a54cdfa40d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d11118f46f3d4010b378ae878e16c1b5", "style": "IPY_MODEL_3be01127e73e4469b5a3a255016c2502", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d11118f46f3d4010b378ae878e16c1b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3be01127e73e4469b5a3a255016c2502": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8bc611a4147146ae99936546e8aa377d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ef9c0f15c3204bf2bd629f7d70ecde9f", "style": "IPY_MODEL_982c62d0a3cd4a638d83d88236d5cda5", "value": "<div>The overall accuracy of your model is: 50.19%.<br>Best accuracy achieved on samples with <b>0</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>1</b> label (0.38%).</div>"}}, "ef9c0f15c3204bf2bd629f7d70ecde9f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "982c62d0a3cd4a638d83d88236d5cda5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b3db0933b2b642fd9efc7cba16617873": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[96.92, 100.0, 96.93], [0.0, 0.0, 0.0]], [[3.07, 99.62, 26500.0], [0.01, 0.38, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[8360, 0], [265, 1]], "texttemplate": "%{text}", "x": ["0", "1"], "xgap": 1, "y": ["0", "1"], "ygap": 1, "z": [["100", "0"], ["99.62", "0.38"]], "type": "heatmap", "uid": "4fb54e58-1961-471f-b702-e55f91c6efd5"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "6968f7ef1e284405805a241c051ef626": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_173a67f533444581bcd5f6c6aff3ae44", "style": "IPY_MODEL_2a65eeb711e34ef2acc3fd4024972588", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "173a67f533444581bcd5f6c6aff3ae44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a65eeb711e34ef2acc3fd4024972588": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2c7eca8021cc4078a6e06b4049aff56f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf0319dc4419452bb07ffd8bc687d0af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e04d622b39f34446883dfafa473df20b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce022423479b40168c4dc3d7d5b079f3", "style": "IPY_MODEL_f31160c5649e4ced913e4601f6db0867", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "ce022423479b40168c4dc3d7d5b079f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f31160c5649e4ced913e4601f6db0867": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a8fe994c51441dbacfa32115e70e9f7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_8734c7cfe94d45c7be67cae3c582a819", "IPY_MODEL_bf8365daa1de4b6799d16d8da26407e1", "IPY_MODEL_949401f3ca334630a5011a95b2085e47"], "layout": "IPY_MODEL_fe6bd02f3d0f4376a807a36f6e4a8ed8"}}, "8734c7cfe94d45c7be67cae3c582a819": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4b2427904fd24f019dd1bfc808807911", "style": "IPY_MODEL_2b0f2ec01fd24eeda96a93a968c2e3e4", "value": "<h4 id=\"ConfusionMatrixReport-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\"><b>Confusion Matrix Report - Test Dataset</b></h4>"}}, "4b2427904fd24f019dd1bfc808807911": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b0f2ec01fd24eeda96a93a968c2e3e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf8365daa1de4b6799d16d8da26407e1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a330dd7dac994aeda7dad3315bb064c1", "style": "IPY_MODEL_4f2f2ef0c513461789c69726d6dcacd6", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a330dd7dac994aeda7dad3315bb064c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f2f2ef0c513461789c69726d6dcacd6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "949401f3ca334630a5011a95b2085e47": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cb5ceb97459f485fbd2dabb247ab1996", "IPY_MODEL_c395f913bf7146feabcc7ac2bf4c8466", "IPY_MODEL_be81a38732c5458eaf2775fbbb3322b7", "IPY_MODEL_8431571a614c4a18a0571b37519ccecb"], "layout": "IPY_MODEL_f6c0ad4346f34b69a86fc47b6e113554"}}, "cb5ceb97459f485fbd2dabb247ab1996": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7f28df4d7b3a4ea896d0337a3e870540", "style": "IPY_MODEL_dc5020cf39ca4bbc91b018810a1f9651", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7f28df4d7b3a4ea896d0337a3e870540": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc5020cf39ca4bbc91b018810a1f9651": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c395f913bf7146feabcc7ac2bf4c8466": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1bc5b90f72ee417db746122ce13c21dc", "style": "IPY_MODEL_38ab0f1da0d64dafb8b5846ae529ce6f", "value": "<div>The overall accuracy of your model is: 51.19%.<br>Best accuracy achieved on samples with <b>0</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>1</b> label (2.38%).</div>"}}, "1bc5b90f72ee417db746122ce13c21dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38ab0f1da0d64dafb8b5846ae529ce6f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be81a38732c5458eaf2775fbbb3322b7": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[96.92, 100.0, 96.99], [0.0, 0.0, 0.0]], [[3.01, 97.62, 4100.0], [0.07, 2.38, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[2640, 0], [82, 2]], "texttemplate": "%{text}", "x": ["0", "1"], "xgap": 1, "y": ["0", "1"], "ygap": 1, "z": [["100", "0"], ["97.62", "2.38"]], "type": "heatmap", "uid": "a072f19d-4718-49f1-9e63-882ab06a9ec5"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", 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"zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "8431571a614c4a18a0571b37519ccecb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1177a5b9287f4410a33b3b79a0a82321", "style": "IPY_MODEL_3810b31031cf4390b839341005211b57", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "1177a5b9287f4410a33b3b79a0a82321": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3810b31031cf4390b839341005211b57": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f6c0ad4346f34b69a86fc47b6e113554": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe6bd02f3d0f4376a807a36f6e4a8ed8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "039eacd6cbbf419e94abb2908ede97e9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_dc0234ba71e64f2b8205c4bed53bbbe9", "IPY_MODEL_022e65bc6a8949ba991aad17fd1786fb", "IPY_MODEL_b4a297623ea4411a917fc3b39e542ab7"], "layout": "IPY_MODEL_cf5ccaa57ca647cabfbccc70cd4f1039"}}, "dc0234ba71e64f2b8205c4bed53bbbe9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7b8a4d24b5814292b181642cc1f29511", "style": "IPY_MODEL_dc0d6aae2abe4a66a29426f4b9342a53", "value": "<h4 id=\"CalibrationMetric-TrainDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\"><b>Calibration Metric - Train Dataset</b></h4>"}}, "7b8a4d24b5814292b181642cc1f29511": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc0d6aae2abe4a66a29426f4b9342a53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "022e65bc6a8949ba991aad17fd1786fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_be33e023198742f0a611dcb5228c2278", "style": "IPY_MODEL_eccf5e98a2cc4664a4f0ce07617f9a2d", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "be33e023198742f0a611dcb5228c2278": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eccf5e98a2cc4664a4f0ce07617f9a2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4a297623ea4411a917fc3b39e542ab7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5b8d222ce8e04a0c8a007f74c2af6a8a", "IPY_MODEL_86badc51b7704a08a74c869bf6ea3de7", "IPY_MODEL_4fb82f1a5e2b4dafb7a8669431c9e979", "IPY_MODEL_cb0a78d76aef48f4a743ea840bae5482", "IPY_MODEL_a2265269ef6a4ab6ac6f09336376bee7"], "layout": "IPY_MODEL_75012c66f1e34d1997fd5678b4de86f1"}}, "5b8d222ce8e04a0c8a007f74c2af6a8a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c2576cc51784c9c89e5299751c1d156", "style": "IPY_MODEL_d2a99f3aa8e9416e9fc3e9285593f752", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9c2576cc51784c9c89e5299751c1d156": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d2a99f3aa8e9416e9fc3e9285593f752": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "86badc51b7704a08a74c869bf6ea3de7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f0c6b6d086b04a599168f0b58dfa0f9a", "style": "IPY_MODEL_fa9532cf48144d54b0029aa80f48f307", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.</div>"}}, "f0c6b6d086b04a599168f0b58dfa0f9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa9532cf48144d54b0029aa80f48f307": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4fb82f1a5e2b4dafb7a8669431c9e979": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "498b3c4d-629d-46a9-b6ad-cc5bb0733029"}, {"mode": "lines+markers", "name": "(brier:   0.0243)", "x": {"dtype": "float64", "shape": [6]}, "y": {"dtype": "float64", "shape": [6]}, "type": "scatter", "uid": 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"zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Calibration plots (reliability curve)"}, "xaxis": {"title": {"text": "Mean predicted value"}}, "yaxis": {"title": {"text": "Fraction of positives"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "22V5yNFflj8FmFD15LvAPylsGTPm480/4XOU342C1T+NHmxaUprbP2UXXYNINuE/"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "um8ewhs7jD8VL5BJ8QLJP1kZvhEFJt0/TjbZZJNN5j8cx3Ecx3HsPwAAAAAAAPA/"}]}, "cb0a78d76aef48f4a743ea840bae5482": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9fcc686ba8cb4a7994f23738ebf79083", "style": "IPY_MODEL_a1f9d3553ba34d84ada288a1bb3fcfdc", "value": "<div>The Brier score metric may be used to assess how well a classifier is calibrated. For more info, please visit https://en.wikipedia.org/wiki/Brier_score</div>"}}, "9fcc686ba8cb4a7994f23738ebf79083": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1f9d3553ba34d84ada288a1bb3fcfdc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a2265269ef6a4ab6ac6f09336376bee7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c9ecad274da4b93bf9286f685455605", "style": "IPY_MODEL_717260853b86416893f9f862c7f5a1a8", "value": "<br><a href=\"#summary_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\" style=\"font-size: 14px;\">Go to top</a>"}}, "9c9ecad274da4b93bf9286f685455605": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "717260853b86416893f9f862c7f5a1a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "75012c66f1e34d1997fd5678b4de86f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf5ccaa57ca647cabfbccc70cd4f1039": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b82c7b97240446709363d0c7b82311a9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_26c6d3d5f4e1484a91b94c0c698b33c5", "IPY_MODEL_58cbdd572f11482d9ddd70bb654d5c45", "IPY_MODEL_928abc4318d547c9bde012d015e7a324"], "layout": "IPY_MODEL_217f820116ad4b459ec573d5a60cc5ac"}}, "26c6d3d5f4e1484a91b94c0c698b33c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7b804111fc13451382574c5e6ac543a0", "style": "IPY_MODEL_ac58e42f8b914615b4ce14f6a94e559d", "value": "<h4 id=\"CalibrationMetric-TestDataset_4W54FUPL7PHXDK18ZE6AI52XM-section-KIUOE\"><b>Calibration Metric - Test Dataset</b></h4>"}}, "7b804111fc13451382574c5e6ac543a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ac58e42f8b914615b4ce14f6a94e559d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58cbdd572f11482d9ddd70bb654d5c45": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_424e91aaaf2c4a639b065d205e2e14ca", "style": "IPY_MODEL_bc8a71c653c94853be0fef0955d69812", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "424e91aaaf2c4a639b065d205e2e14ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc8a71c653c94853be0fef0955d69812": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "928abc4318d547c9bde012d015e7a324": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_fb213b863b584ca7bff15561b589b00a", "IPY_MODEL_bfb1eb4ae69d4b07a64452ab4808224c", "IPY_MODEL_206f22f1fb664e7ab2adee5f21ae5359", "IPY_MODEL_be2cfc6b898642fb9e8d6c8e8eda3f63", "IPY_MODEL_2489465f1e2440dfaa0a2b371d32aadf"], "layout": "IPY_MODEL_a26fafebfd2f4c3a98be331b4a5a0553"}}, "fb213b863b584ca7bff15561b589b00a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_062d661dbaf04afeba2e33b600c7ad32", "style": "IPY_MODEL_d21e795f3c034466872bf9edb7f96572", "value": "<h5><b>Additional Outputs</b></h5>"}}, "062d661dbaf04afeba2e33b600c7ad32": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d21e795f3c034466872bf9edb7f96572": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bfb1eb4ae69d4b07a64452ab4808224c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_09291af2c462435aa8e5d10d4969fa09", "style": "IPY_MODEL_737a3683d333429db3221204c06dec11", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.</div>"}}, "09291af2c462435aa8e5d10d4969fa09": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "737a3683d333429db3221204c06dec11": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "206f22f1fb664e7ab2adee5f21ae5359": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "7164d098-677f-49cd-87e1-253dab29018c"}, {"mode": "lines+markers", "name": "(brier:   0.0242)", "x": {"dtype": "float64", "shape": [6]}, "y": {"dtype": "float64", "shape": [6]}, "type": "scatter", "uid": 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.. GENERATED FROM PYTHON SOURCE LINES 335-363

Understanding the checks' results!
==================================
Ok! Now that we're back on track lets run some performance checks to see how we did.

* ``Simple Model Comparison`` - This checks make sure our model outperforms a very
  simple model to some degree. Having it fail means we might have a serious problem.
* ``Model Error Analysis`` - This check analyses model errors and tries to find a
  way to segment our data in a way that is informative to error analysis. It seems
  that it found a valuable way to segment our data, error-wise, using the ``urlLength``
  feature. We'll look into it soon enough.

Looking at the metric plots for F1 for both our model and a simple one we see their
performance are almost identical! How can this be? Fortunately the confusion
matrices automagically generated for both the training and test sets help us
understand what has happened.

Our evidently over-regularized classifier was over-impressed by the majority class
(0, or non-malicious URL), and predicted a value of 0 for almost all samples in
both the train and the test set, which yielded a seemingly-impressive 97% accuracy
on the test set just due to the imbalanced nature of the problem.

``deepchecks`` also generated plots for F1, precision and recall on both the train
and test set, as part of the performance report, and these also help us see
recall scores are almost zero for both sets and understand what happened.

Trying out a different classifier
=================================
So let's throw something a bit more rich in expressive power at the problem - a decision tree!

.. GENERATED FROM PYTHON SOURCE LINES 363-370

.. code-block:: default


    from sklearn.tree import DecisionTreeClassifier

    model = DecisionTreeClassifier(criterion='entropy', splitter='random', random_state=SEED)
    model.fit(train_X, train_y)
    msuite.run(model=model, train_dataset=ds_train, test_dataset=ds_test)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Model Evaluation Suite:
    |           | 0/11 [Time: 00:00]
    Model Evaluation Suite:
    |█          | 1/11 [Time: 00:00, Check=Train Test Performance]
    Model Evaluation Suite:
    |████       | 4/11 [Time: 00:00, Check=Prediction Drift]      
    Model Evaluation Suite:
    |████       | 4/11 [Time: 00:13, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |██████     | 6/11 [Time: 00:58, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |█████████  | 9/11 [Time: 00:58, Check=Unused Features]          
                                                                               

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"model_module_version": "1.5.0", "state": {}}, "9c1c4b89ce42427eb55ce35d017948aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81846f4775674895aaeb68eeadb1ec5f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "823ab0de6ffe448a9472d8b5f3b693c3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_de57e334c7ef44b1b3962599a70ee783", "style": "IPY_MODEL_c4529aa93ebc41cca2b3c60a94513ef1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "de57e334c7ef44b1b3962599a70ee783": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c4529aa93ebc41cca2b3c60a94513ef1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "41626d4007e744009699aa0f1be6a1f4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_6af7e7c3059142148e5a52f46b64ca42", "IPY_MODEL_11909fd7fe934b24bd73e9763b1a6f48", "IPY_MODEL_75513e5065ff433b8b0e94eb16c7468f", "IPY_MODEL_497a60d4913b40efb3d5f79a2b562b2b"], "layout": "IPY_MODEL_35bca12f3bdc4333a3a4c446df406019"}}, "6af7e7c3059142148e5a52f46b64ca42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3bb99a200b8744c2be842937ec0d12e0", "style": "IPY_MODEL_df32751d28bb4b91aa5df231d91a50cd", "value": "<h4 id=\"UnusedFeatures-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-SU4D8\"><b>Unused Features - Test Dataset</b></h4>"}}, "3bb99a200b8744c2be842937ec0d12e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df32751d28bb4b91aa5df231d91a50cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "11909fd7fe934b24bd73e9763b1a6f48": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d165ace01b6442caa2866047fabc1443", "style": "IPY_MODEL_7395428b965d44ccae87d5d4c4e9de3f", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d165ace01b6442caa2866047fabc1443": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7395428b965d44ccae87d5d4c4e9de3f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "75513e5065ff433b8b0e94eb16c7468f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bdd859d1c88847dfba3636cbd979c89d", "style": "IPY_MODEL_d6d32c54f4df43458cc82fe5ed1c329e", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_ab1c4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ab1c4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ab1c4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ab1c4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ab1c4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ab1c4_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_ab1c4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_ab1c4_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_ab1c4_row0_col2\" class=\"data row0 col2\" >Found 6 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bdd859d1c88847dfba3636cbd979c89d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d6d32c54f4df43458cc82fe5ed1c329e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "497a60d4913b40efb3d5f79a2b562b2b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a0c368f47c51489d83025103580dc842", "IPY_MODEL_b58efa0c0b2b4506b0341b299949a02b", "IPY_MODEL_4389b504c3e843d9a1fc0c76231a96bf", "IPY_MODEL_6bff90df037c4c65b6de01106929d1a9"], "layout": "IPY_MODEL_710eaae2fe204615951802b23306e667"}}, "a0c368f47c51489d83025103580dc842": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_28d051b68e7f410993ee27a839e8e899", "style": "IPY_MODEL_4f37390ad60c4555928d0e1d2d8e3ed1", "value": "<h5><b>Additional Outputs</b></h5>"}}, "28d051b68e7f410993ee27a839e8e899": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f37390ad60c4555928d0e1d2d8e3ed1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b58efa0c0b2b4506b0341b299949a02b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7823c0bbb753465d9f59b660637176fd", "style": "IPY_MODEL_08df842cdb624f1e85e1e0fee7dc1a16", "value": "<div>Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters</div>"}}, "7823c0bbb753465d9f59b660637176fd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "08df842cdb624f1e85e1e0fee7dc1a16": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4389b504c3e843d9a1fc0c76231a96bf": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", 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"IPY_MODEL_89ec7f072e3c4ce985a5fa435b5e771f", "IPY_MODEL_e3bd8514f1ae48ebb6ec22b4c32a8064", "IPY_MODEL_89ec7f072e3c4ce985a5fa435b5e771f", "IPY_MODEL_59180615fc6f487d81b70613743fbd10", "IPY_MODEL_89ec7f072e3c4ce985a5fa435b5e771f", "IPY_MODEL_77674f4e3ded4918aa7567d48208acee", "IPY_MODEL_89ec7f072e3c4ce985a5fa435b5e771f", "IPY_MODEL_e56544c6b82246febd25c575635ea142", "IPY_MODEL_89ec7f072e3c4ce985a5fa435b5e771f", "IPY_MODEL_15db8415fefc48bbb4d66d93f1b7d34b", "IPY_MODEL_89ec7f072e3c4ce985a5fa435b5e771f", "IPY_MODEL_ee024fecb9a54ae986b2ce9180859159"], "layout": "IPY_MODEL_b9201f538d9e454eae88004303e40a87"}}, "9b0b325bd4fe422f8ebbc91f27305b9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cdb0053e09c84cc4b15d8cb24dc0ed53", "style": "IPY_MODEL_265f85994f624e66a113c360d18513ed", "value": "<style type=\"text/css\">\n#T_bc3f9_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc3f9_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc3f9_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc3f9_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc3f9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_bc3f9_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_bc3f9_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row0_col1\" class=\"data row0 col1\" ><a href=\"#ROCReport-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">ROC Report - Train Dataset</a></td>\n      <td id=\"T_bc3f9_row0_col2\" class=\"data row0 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_bc3f9_row0_col3\" class=\"data row0 col3\" >All classes passed, minimum AUC found is 1 for class 1</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row1_col1\" class=\"data row1 col1\" ><a href=\"#ROCReport-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">ROC Report - Test Dataset</a></td>\n      <td id=\"T_bc3f9_row1_col2\" class=\"data row1 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_bc3f9_row1_col3\" class=\"data row1 col3\" >All classes passed, minimum AUC found is 0.92 for class 1</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row2_col1\" class=\"data row2 col1\" ><a href=\"#PredictionDrift_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">Prediction Drift</a></td>\n      <td id=\"T_bc3f9_row2_col2\" class=\"data row2 col2\" >Prediction drift score < 0.15</td>\n      <td id=\"T_bc3f9_row2_col3\" class=\"data row2 col3\" >Found model prediction Kolmogorov-Smirnov drift score of 2.20E-3</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row3_col1\" class=\"data row3 col1\" ><a href=\"#SimpleModelComparison_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">Simple Model Comparison</a></td>\n      <td id=\"T_bc3f9_row3_col2\" class=\"data row3 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_bc3f9_row3_col3\" class=\"data row3 col3\" >All classes passed, average gain for metrics: {'F1': '71.43%'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row4_col1\" class=\"data row4 col1\" ><a href=\"#WeakSegmentsPerformance-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">Weak Segments Performance - Test Dataset</a></td>\n      <td id=\"T_bc3f9_row4_col2\" class=\"data row4 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_bc3f9_row4_col3\" class=\"data row4 col3\" >Found a segment with accuracy score of 0.92 in comparison to an average score of 0.988 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row5_col1\" class=\"data row5 col1\" ><a href=\"#UnusedFeatures-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">Unused Features - Train Dataset</a></td>\n      <td id=\"T_bc3f9_row5_col2\" class=\"data row5 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_bc3f9_row5_col3\" class=\"data row5 col3\" >Found 5 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_bc3f9_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_bc3f9_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 1.55e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_bc3f9_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bc3f9_row7_col1\" class=\"data row7 col1\" ><a href=\"#ModelInferenceTime-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_bc3f9_row7_col2\" class=\"data row7 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_bc3f9_row7_col3\" class=\"data row7 col3\" >Found average inference time (seconds): 1.46e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "cdb0053e09c84cc4b15d8cb24dc0ed53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "265f85994f624e66a113c360d18513ed": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5fa3faffa6ba47238444e8984ecdbe1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd19dbf656654fdcad80cc0bc26ec218", "style": "IPY_MODEL_50ba56e149ae44b5b10b375203202431", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "fd19dbf656654fdcad80cc0bc26ec218": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "50ba56e149ae44b5b10b375203202431": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0980eaa8708449618b2c456c9bd19ec7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_7b72cf8b63e14c449c336f099f717463", "IPY_MODEL_c50a3dd00e2746349b6b1af8b1ea3e14", "IPY_MODEL_85f58fa9a2424d91bb7b6c6ecbd9d2b2", "IPY_MODEL_60c8fb0e6b6e425baff6894d2d585e7d"], "layout": "IPY_MODEL_9fe073b763ef4043acad6023ac4eb890"}}, "7b72cf8b63e14c449c336f099f717463": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a11dcd53a34422993423c31b8f96831", "style": "IPY_MODEL_43dff20f3ceb4887975b1edd4adf70b4", "value": "<h4 id=\"ROCReport-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>ROC Report - Train Dataset</b></h4>"}}, "5a11dcd53a34422993423c31b8f96831": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43dff20f3ceb4887975b1edd4adf70b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c50a3dd00e2746349b6b1af8b1ea3e14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0fcb9eaedeba4b37bd84e90984a99200", "style": "IPY_MODEL_cdb747c7d1374d209562eaf65b57d8d3", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "0fcb9eaedeba4b37bd84e90984a99200": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cdb747c7d1374d209562eaf65b57d8d3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85f58fa9a2424d91bb7b6c6ecbd9d2b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bf387a3d44a34fc09257b811f5cbad5e", "style": "IPY_MODEL_60a2a444fe5c4cc8bd96d9c09af34c15", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_83e8b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_83e8b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_83e8b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_83e8b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_83e8b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_83e8b_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_83e8b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_83e8b_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_83e8b_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 1 for class 1</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bf387a3d44a34fc09257b811f5cbad5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "60a2a444fe5c4cc8bd96d9c09af34c15": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "60c8fb0e6b6e425baff6894d2d585e7d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_12ccc3da778845cda729caba79718e37", "IPY_MODEL_8c663016bc2b435a937021243e35d188", "IPY_MODEL_f42dcfc933204632bd0d7370bc606395", "IPY_MODEL_d994bb8de6224667b42393937393204f"], "layout": "IPY_MODEL_c3ea5a43b1c7432c94e1001762f69b65"}}, "12ccc3da778845cda729caba79718e37": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a1fd45c54f604b43b067381a48e808a8", "style": "IPY_MODEL_9658475b79e84336b10824ee9b02d56a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a1fd45c54f604b43b067381a48e808a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9658475b79e84336b10824ee9b02d56a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8c663016bc2b435a937021243e35d188": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "Class 1 (auc = 1.00)", "x": {"dtype": "float64", "shape": [3]}, "y": {"dtype": "float64", "shape": [3]}, "type": "scatter", "uid": "e1792869-893a-4348-819f-21dcd4962563"}, {"hovertemplate": "Class: 1<br>TPR: %{y:.2%}<br>FPR: %{x:.2%}<br>Optimal Threshold: 1.0", "marker": {"color": "black", "size": 15}, "mode": "markers", "showlegend": false, "x": [0.0], "y": [1.0], "type": "scatter", "uid": "10374be4-2fe1-475d-a915-1aca9ac58207"}, {"line": {"color": "#444", "dash": "dash", "width": 2}, "showlegend": false, "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "e6ee627b-ff34-4264-98e4-97cbb17ac539"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 500, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], 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{"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Receiver Operating Characteristic Plot"}, "xaxis": {"title": {"text": "False Positive Rate"}}, "yaxis": {"title": {"text": "True Positive Rate"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAPA/"}, {"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPwAAAAAAAPA/"}]}, "f42dcfc933204632bd0d7370bc606395": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_38879d8179fc479a94e6d6931a2afa9d", "style": "IPY_MODEL_f2f1b523d51f4138b39fd17d09c00604", "value": "<div>The marked points are the optimal probability threshold cut-off points to predict said\n            class. In plain terms, it is optimal to set the prediction rule such that if for some class the predicted\n            probability is above the threshold of that class, then the prediction should be that class.\n            They optimal thresholds are determined using Youden's index defined as sensitivity + specificity - 1.</div>"}}, "38879d8179fc479a94e6d6931a2afa9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2f1b523d51f4138b39fd17d09c00604": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d994bb8de6224667b42393937393204f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ad460b7ff6db4d5192afbd0cc10a6112", "style": "IPY_MODEL_ec8806cc973b431191e94c99daa0f626", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "ad460b7ff6db4d5192afbd0cc10a6112": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec8806cc973b431191e94c99daa0f626": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c3ea5a43b1c7432c94e1001762f69b65": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9fe073b763ef4043acad6023ac4eb890": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "89ec7f072e3c4ce985a5fa435b5e771f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a07f2cfc066740408466eed5a8d22441", "style": "IPY_MODEL_547832ce430346e384a516bbe8d13e17", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a07f2cfc066740408466eed5a8d22441": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "547832ce430346e384a516bbe8d13e17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c8f5b5d853f49c0b2c0335795049887": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_4e95587044434bf7a8802dd2e52d1686", "IPY_MODEL_9412383c27954fcf8f347787ae835629", "IPY_MODEL_9a86ad9a594741cea04fbeffbe5431a4", "IPY_MODEL_680a60b78d4547bd8369e3c25d24d816"], "layout": "IPY_MODEL_e7e4b3a64b48442895bb533fe3a2dd98"}}, "4e95587044434bf7a8802dd2e52d1686": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_74c4bfea42d748ff8b24acdbf287c1a6", "style": "IPY_MODEL_1c37d835c2044a6b84ee32111245ccae", "value": "<h4 id=\"ROCReport-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>ROC Report - Test Dataset</b></h4>"}}, "74c4bfea42d748ff8b24acdbf287c1a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1c37d835c2044a6b84ee32111245ccae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9412383c27954fcf8f347787ae835629": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cef01bc85962428485c3f2e5f801cbdd", "style": "IPY_MODEL_74bafb83d87a4b388864a5797b99119e", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cef01bc85962428485c3f2e5f801cbdd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "74bafb83d87a4b388864a5797b99119e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a86ad9a594741cea04fbeffbe5431a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0fdea5f042534afca9acba1b0c7bdf26", "style": "IPY_MODEL_788303e05aba43e99b9f7611aa97b1e5", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_22ea6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_22ea6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_22ea6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_22ea6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_22ea6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_22ea6_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_22ea6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_22ea6_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_22ea6_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 0.92 for class 1</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0fdea5f042534afca9acba1b0c7bdf26": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "788303e05aba43e99b9f7611aa97b1e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "680a60b78d4547bd8369e3c25d24d816": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d42b7336535a4eb09748bcd720e03fe3", "IPY_MODEL_a3d31eb3d65840b8b5f57cd54d999756", "IPY_MODEL_fb5411d090e043dc9a1f81951fed7a0e", "IPY_MODEL_d963e44cc1874d63aeb77b6388c40caf"], "layout": "IPY_MODEL_127be633c2004f05860f9c30563977e9"}}, "d42b7336535a4eb09748bcd720e03fe3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7ad8d1599e0543158717ac69f835cc19", "style": "IPY_MODEL_f1971a9e4edd4149b11f37e519ed2a1c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7ad8d1599e0543158717ac69f835cc19": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1971a9e4edd4149b11f37e519ed2a1c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a3d31eb3d65840b8b5f57cd54d999756": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "Class 1 (auc = 0.92)", "x": {"dtype": "float64", "shape": [3]}, "y": {"dtype": "float64", "shape": [3]}, "type": "scatter", "uid": "e9ec9350-27a3-475f-bd78-0d92bbd14940"}, {"hovertemplate": "Class: 1<br>TPR: %{y:.2%}<br>FPR: %{x:.2%}<br>Optimal Threshold: 1.0", "marker": {"color": "black", "size": 15}, "mode": "markers", "showlegend": false, "x": [0.007196969696969697], "y": [0.8452380952380952], "type": "scatter", "uid": "7ea60eb5-c53d-4024-9e1e-b8d7e0fe6f45"}, {"line": {"color": "#444", "dash": "dash", "width": 2}, "showlegend": false, "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "e5f2a621-74f2-458a-bd70-841205f6cc32"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 500, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], 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"scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Receiver Operating Characteristic Plot"}, "xaxis": {"title": {"text": "False Positive Rate"}}, "yaxis": {"title": {"text": "True Positive Rate"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": "AAAAAAAAAAB7HanXkXp9PwAAAAAAAPA/"}, {"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "AAAAAAAAAADDMAzDMAzrPwAAAAAAAPA/"}]}, "fb5411d090e043dc9a1f81951fed7a0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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In plain terms, it is optimal to set the prediction rule such that if for some class the predicted\n            probability is above the threshold of that class, then the prediction should be that class.\n            They optimal thresholds are determined using Youden's index defined as sensitivity + specificity - 1.</div>"}}, "36a741cd9ad1437f981c285b0f5f7cd3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "baf7f880d00d456392c272a989e865dc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d963e44cc1874d63aeb77b6388c40caf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2ea399f4a58b42318258754550908a44", "style": "IPY_MODEL_de83f619766841cd86d368f73fab4141", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "2ea399f4a58b42318258754550908a44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de83f619766841cd86d368f73fab4141": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "127be633c2004f05860f9c30563977e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e7e4b3a64b48442895bb533fe3a2dd98": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3bd8514f1ae48ebb6ec22b4c32a8064": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_5bf9a4e41a8e42c898156fdbd938f33b", "IPY_MODEL_0dbe2fbb115e4fe7bebfdb927c0315ab", "IPY_MODEL_d6905bbe7bb2424ba1a488183051dfc0", "IPY_MODEL_4a18854bce12484994afe269fd5560e8"], "layout": "IPY_MODEL_33bfbaf02a994a9b8608c966b0df63e6"}}, "5bf9a4e41a8e42c898156fdbd938f33b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_543201a9200f459cb61914ac6d194fa9", "style": "IPY_MODEL_9ff3c4bda87346ef9e4437d0c6572456", "value": "<h4 id=\"PredictionDrift_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>Prediction Drift</b></h4>"}}, "543201a9200f459cb61914ac6d194fa9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ff3c4bda87346ef9e4437d0c6572456": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0dbe2fbb115e4fe7bebfdb927c0315ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a9ee81b99c414538844d8ca11520b8fa", "style": "IPY_MODEL_a1a36da1e8f64913bf8c9ce04869df25", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a9ee81b99c414538844d8ca11520b8fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1a36da1e8f64913bf8c9ce04869df25": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d6905bbe7bb2424ba1a488183051dfc0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0226768f3b3a4f578d262303ae42a11f", "style": "IPY_MODEL_232c4234684f480b94a83ad54665b7d6", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_426fc_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_426fc_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_426fc_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_426fc_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_426fc_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_426fc_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_426fc_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_426fc_row0_col1\" class=\"data row0 col1\" >Prediction drift score < 0.15</td>\n      <td id=\"T_426fc_row0_col2\" class=\"data row0 col2\" >Found model prediction Kolmogorov-Smirnov drift score of 2.20E-3</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0226768f3b3a4f578d262303ae42a11f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "232c4234684f480b94a83ad54665b7d6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4a18854bce12484994afe269fd5560e8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4bc9daec1d454a8faf2d27fbb025a915", "IPY_MODEL_f30db39f24eb47648b25c6aa7aa4c9d5", "IPY_MODEL_88aa06c430bb4b2b81de356084a09f4a", "IPY_MODEL_cda6435929e4457083a289f75d6d0c06", "IPY_MODEL_eac2ed690df147cd98c5be72c162f66f"], "layout": "IPY_MODEL_3c41e9a3ed4246cca7f746dd34c646da"}}, "4bc9daec1d454a8faf2d27fbb025a915": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5074b867f4364e0194dde63260ad1faf", "style": "IPY_MODEL_2a0e6b76d7294fa8a74d45a05e976c89", "value": "<h5><b>Additional Outputs</b></h5>"}}, "5074b867f4364e0194dde63260ad1faf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a0e6b76d7294fa8a74d45a05e976c89": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f30db39f24eb47648b25c6aa7aa4c9d5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e60659d076464128bb2c7bc735b783f8", "style": "IPY_MODEL_acbcc09eaaa541f8a3cbda1a26f8bd65", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the predicted\n                class probabilities.\n            </span></div>"}}, "e60659d076464128bb2c7bc735b783f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "acbcc09eaaa541f8a3cbda1a26f8bd65": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88aa06c430bb4b2b81de356084a09f4a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_59207d6e003b48f3b07a398769da310d", "style": "IPY_MODEL_b17af424574d455a9d53e2f7d0848d8d", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "59207d6e003b48f3b07a398769da310d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b17af424574d455a9d53e2f7d0848d8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cda6435929e4457083a289f75d6d0c06": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0022026431718061845], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "9000dd11-deea-4e96-8589-28a88990ca17"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": {"dtype": "float64", "shape": [2]}, "xaxis": "x2", 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["_data", 2, "y", "buffer"], "data": "s+tF1lbx7j/LRKGbkuqgPw=="}, {"encoding": "base64", "path": ["_layout", "xaxis2", "tickvals", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}]}, "eac2ed690df147cd98c5be72c162f66f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_975550bf28204ab5b4222ecf5f0d2d7e", "style": "IPY_MODEL_3ee7160e641f4069a81add9c48474c8d", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "975550bf28204ab5b4222ecf5f0d2d7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ee7160e641f4069a81add9c48474c8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c41e9a3ed4246cca7f746dd34c646da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33bfbaf02a994a9b8608c966b0df63e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59180615fc6f487d81b70613743fbd10": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2c8e2cfd5239462ab2cc5a1a3fed73ca", "IPY_MODEL_bc52b12fb48948ec9a38a300250dd579", "IPY_MODEL_ccf1fd60915540459df33688a473d09b", "IPY_MODEL_12c46280bc00489399f7f255b2a970e8"], "layout": "IPY_MODEL_712fea1a7c67481ba463d3c8971ea20d"}}, "2c8e2cfd5239462ab2cc5a1a3fed73ca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_46e41741887b472793913eea35871ae9", "style": "IPY_MODEL_d4eeb6170dc841549eb54bbe1343cba9", "value": "<h4 id=\"SimpleModelComparison_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>Simple Model Comparison</b></h4>"}}, "46e41741887b472793913eea35871ae9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d4eeb6170dc841549eb54bbe1343cba9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bc52b12fb48948ec9a38a300250dd579": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10571a23e2fc44e88b31ac825869f881", "style": "IPY_MODEL_490f214fbadb45ea9b529de023b197d6", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "10571a23e2fc44e88b31ac825869f881": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "490f214fbadb45ea9b529de023b197d6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ccf1fd60915540459df33688a473d09b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c9966f20c9045d683ee786826cbab9c", "style": "IPY_MODEL_fc1ac84ee09d454bba6ce91bc6d4764f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3de48_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3de48_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3de48_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3de48_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3de48_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3de48_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3de48_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3de48_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_3de48_row0_col2\" class=\"data row0 col2\" >All classes passed, average gain for metrics: {'F1': '71.43%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1c9966f20c9045d683ee786826cbab9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc1ac84ee09d454bba6ce91bc6d4764f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12c46280bc00489399f7f255b2a970e8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4d99a6742be74b56a9ad443738f13d3d", "IPY_MODEL_b339815e8b1c43a5991846f0cec572d7", "IPY_MODEL_34ebc549b2234f0681ca98007f81e7f3"], "layout": "IPY_MODEL_def9317c61d54eed8ac124044fc0504f"}}, "4d99a6742be74b56a9ad443738f13d3d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ec88fe75c62f48f9b2a37a22185b30f3", "style": "IPY_MODEL_9665e6ca2d3a490cb8dc17b66ce2486c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ec88fe75c62f48f9b2a37a22185b30f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9665e6ca2d3a490cb8dc17b66ce2486c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b339815e8b1c43a5991846f0cec572d7": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=DecisionTreeClassifier model<br>Metric=F1<br>value=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "DecisionTreeClassifier model", "marker": {"color": "#636efa", "pattern": {"shape": ""}}, "name": "DecisionTreeClassifier model", "offsetgroup": "DecisionTreeClassifier model", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "31393854-d1f4-4fce-b2ed-2e5bfcc1c343"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple model - most_frequent<br>Metric=F1<br>value=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple model - most_frequent", "marker": {"color": "#EF553B", "pattern": {"shape": ""}}, "name": "Simple model - most_frequent", "offsetgroup": "Simple model - most_frequent", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "d287feb8-a0b0-4377-974e-15e99ed76571"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "tickangle": 60, "tickprefix": "Class ", "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "k4I7h0vO7z/N1qHfbB3qPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "ugWKsbZ/7z8AAAAAAAAAAA=="}]}, "34ebc549b2234f0681ca98007f81e7f3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c4aa7049326f4465a720454cee48e561", "style": "IPY_MODEL_6c53a806dbc14606ac7c955988b26194", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "c4aa7049326f4465a720454cee48e561": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6c53a806dbc14606ac7c955988b26194": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "def9317c61d54eed8ac124044fc0504f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "712fea1a7c67481ba463d3c8971ea20d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "77674f4e3ded4918aa7567d48208acee": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_1bac684ed73c4a65a09927c21c27fcab", "IPY_MODEL_a939e741454c42efb8fec00e50cc9501", "IPY_MODEL_1973d2d2d0724356bb1afe1bef7fa259", "IPY_MODEL_3ede934f6e6141adb744e0eac782df0c"], "layout": "IPY_MODEL_1d8d009b6312409bbe06d9a1a903fbb6"}}, "1bac684ed73c4a65a09927c21c27fcab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_467e21b2a90349c9adbb8709911282b4", "style": "IPY_MODEL_2372805b91a04bf38773a6dbda3605b9", "value": "<h4 id=\"WeakSegmentsPerformance-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>Weak Segments Performance - Test Dataset</b></h4>"}}, "467e21b2a90349c9adbb8709911282b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2372805b91a04bf38773a6dbda3605b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a939e741454c42efb8fec00e50cc9501": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_45edc04639384da3a56aed957cf9a91b", "style": "IPY_MODEL_c03cc004aa9345f697d279c9cbea7a8d", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "45edc04639384da3a56aed957cf9a91b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c03cc004aa9345f697d279c9cbea7a8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1973d2d2d0724356bb1afe1bef7fa259": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fc4f5bbbc7ad476fbc2510e393e979da", "style": "IPY_MODEL_03dfac2242f042ec88b5d74ff36aa86a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_1f6cd_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1f6cd_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1f6cd_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1f6cd_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1f6cd_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1f6cd_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_1f6cd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1f6cd_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_1f6cd_row0_col2\" class=\"data row0 col2\" >Found a segment with accuracy score of 0.92 in comparison to an average score of 0.988 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "fc4f5bbbc7ad476fbc2510e393e979da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03dfac2242f042ec88b5d74ff36aa86a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3ede934f6e6141adb744e0eac782df0c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_79007597b6754a5ebfd6abee0ebeee0e", "IPY_MODEL_c7c203f5d76743ccaf2f5549949638ad", "IPY_MODEL_79122b5883b64396849ec7e20c88c72d", "IPY_MODEL_31e07d67ae444bbf933d396a18cefb0b"], "layout": "IPY_MODEL_4bd2a784a1a2439a96dadd5bb0b7f5ee"}}, "79007597b6754a5ebfd6abee0ebeee0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_479af71e576a42ec8d5f5b7ebbd6962a", "style": "IPY_MODEL_97efc686cf6d4bf7b10ed489e907573e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "479af71e576a42ec8d5f5b7ebbd6962a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "97efc686cf6d4bf7b10ed489e907573e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c7c203f5d76743ccaf2f5549949638ad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5a8816424ae45338f709e866ee6a66e", "style": "IPY_MODEL_35e73b7e0d044ec7995442e0234409a8", "value": "<div>Showcasing intersections of features with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "a5a8816424ae45338f709e866ee6a66e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "35e73b7e0d044ec7995442e0234409a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79122b5883b64396849ec7e20c88c72d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5c8d7bcb8d8e4dee913b1d7ba7739119", "IPY_MODEL_6aed473220d24c31ad97e5477886b5a1"], "layout": "IPY_MODEL_8f697daea2e1481eb5f5df8d9dd9f166"}}, "5c8d7bcb8d8e4dee913b1d7ba7739119": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ba84864a97bf4c7cbb688368452af4a4", "style": "IPY_MODEL_dd79ae982bd744b69f1499fcefe71a1f", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, 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[1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Accuracy Score (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "entropy"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false, "title": {"text": "dsr"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "fdc34995216446dfb9908a27795372a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e9ffe0a3d004e28b3a8dd00c20de675", "style": "IPY_MODEL_25d570c2ccfc4ca399f163ab02812cb2", "value": "<div>Check ran on 2724 data samples. Accuracy Score on the full data set is 0.99.</div>"}}, "4e9ffe0a3d004e28b3a8dd00c20de675": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25d570c2ccfc4ca399f163ab02812cb2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "233680cd41f34fca8758a7c853b2eb8b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "983d26a3f70b4fdab78a1e1839a0b859": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f697daea2e1481eb5f5df8d9dd9f166": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "31e07d67ae444bbf933d396a18cefb0b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_49cfa68aa1394ceab8d25656997e2700", "style": "IPY_MODEL_07b0311624a14eec938f1892e88efaf3", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "49cfa68aa1394ceab8d25656997e2700": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07b0311624a14eec938f1892e88efaf3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4bd2a784a1a2439a96dadd5bb0b7f5ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1d8d009b6312409bbe06d9a1a903fbb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e56544c6b82246febd25c575635ea142": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_e7d8899d5be94d39a3f11999c6e8972e", "IPY_MODEL_605e4737875e474883a1baafb875d18e", "IPY_MODEL_682ed849354b4d26a88bbc3880922c8e", "IPY_MODEL_3c7e608384af41e398c67481ca988d5d"], "layout": "IPY_MODEL_a726961996344a7580b068a5dd16e70c"}}, "e7d8899d5be94d39a3f11999c6e8972e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_54dcd0fe808f4cb2a622a0052fc0a3e0", "style": "IPY_MODEL_d3b3b8dc9dd74763a174721f13653756", "value": "<h4 id=\"UnusedFeatures-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>Unused Features - Train Dataset</b></h4>"}}, "54dcd0fe808f4cb2a622a0052fc0a3e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3b3b8dc9dd74763a174721f13653756": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "605e4737875e474883a1baafb875d18e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7577f685531141dd998232be6c77f932", "style": "IPY_MODEL_29c3b4113e8747219d32f0b062168ef2", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7577f685531141dd998232be6c77f932": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "29c3b4113e8747219d32f0b062168ef2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "682ed849354b4d26a88bbc3880922c8e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_70648063ee824fee9482f3c0f07cf2ab", "style": "IPY_MODEL_a9279947c99b4634aa950007779af567", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_dd128_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd128_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd128_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd128_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dd128_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_dd128_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_dd128_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_dd128_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_dd128_row0_col2\" class=\"data row0 col2\" >Found 5 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "70648063ee824fee9482f3c0f07cf2ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a9279947c99b4634aa950007779af567": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c7e608384af41e398c67481ca988d5d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b87025d4b39f4ed1adc1cb6300fc1b2e", "IPY_MODEL_f843ea0a88864c34bb21e7b6fcabc11a", "IPY_MODEL_57f61de1a3e74e718182144a5cfa6eb0", "IPY_MODEL_63936ab5a9d84a24b6c400cc4662b86f"], "layout": "IPY_MODEL_9093649efdee45e9ab364b9749e7d87f"}}, "b87025d4b39f4ed1adc1cb6300fc1b2e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b7eb8198be14897bf64c220542eb8e3", "style": "IPY_MODEL_588eacabecf74222a760101d59893574", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1b7eb8198be14897bf64c220542eb8e3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "588eacabecf74222a760101d59893574": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f843ea0a88864c34bb21e7b6fcabc11a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d517e55f447c48e396c9b818b41bc2f1", "style": "IPY_MODEL_f30b1086ab01460aa44c9c1b2d61f088", "value": "<div>Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters</div>"}}, "d517e55f447c48e396c9b818b41bc2f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f30b1086ab01460aa44c9c1b2d61f088": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57f61de1a3e74e718182144a5cfa6eb0": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "indianred"}, "name": "Feature Importance %", "orientation": "h", "x": {"dtype": "float64", "shape": [10]}, "y": ["urlLength", "sbr", "numDigits", "entropy", "specialChars", "scriptLength", "numImages", "sscr", "ext_info", "hasHttp"], "type": "bar", "uid": "96effca3-95d5-4b46-a634-45c3c0032fb3"}, {"marker": {"color": "lightsalmon"}, "name": "Feature Variance %", "orientation": "h", "x": {"dtype": "float64", "shape": [10]}, "y": ["urlLength", "sbr", "numDigits", "entropy", "specialChars", "scriptLength", "numImages", "sscr", "ext_info", "hasHttp"], "type": "bar", "uid": "d3b17c9a-f0c0-4e07-ae14-2ed2738a3aa6"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Last shown significant feature", "x": 1, "xanchor": "right", "xref": "x domain", "y": 4.5, "yanchor": "bottom", "yref": "y"}], "height": 500, "shapes": [{"line": {"color": "green", "dash": "dash", "width": 2}, "type": "line", "x0": 0, "x1": 1, "xref": "x domain", "y0": 4.5, "y1": 4.5, "yref": "y"}], "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Unused features compared to top important features"}, "yaxis": {"autorange": "reversed"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": "rv4bkxVjPUB67puiUDUmQNZzEBWjCCJAeOlK3QBGGUDnyL2sRrUVQAAAAAAAAAAAcIrYhnoR5j+XSPzCK6POPwAAAAAAAAAAAAAAAAAAAAA="}, {"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "RvnFDQbtCEBnPJp1u2/5P39S5B06ogtAzDsfBDygB0AO1H0+XjgWQC1lK/vqDhtAzSzdfH96FkDq5JtapM8SQN9fWAB8Ag1As1dNr/9HC0A="}]}, "63936ab5a9d84a24b6c400cc4662b86f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f11dbd4ef07046e5b03e69097156c292", "style": "IPY_MODEL_5476be01ee6d49028b1225898498652e", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "f11dbd4ef07046e5b03e69097156c292": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5476be01ee6d49028b1225898498652e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9093649efdee45e9ab364b9749e7d87f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a726961996344a7580b068a5dd16e70c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "15db8415fefc48bbb4d66d93f1b7d34b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_cf939966ff294e48ab4ff4c69c603373", "IPY_MODEL_e36271fcf75144a8aa8c7e38c45fd436", "IPY_MODEL_e69cf450b758423d97f1b844a04ff5af", "IPY_MODEL_f76686b188ae4a278ff11e07380f49d8"], "layout": "IPY_MODEL_4c762bd3f8634d9ca3ad28d1db4a6230"}}, "cf939966ff294e48ab4ff4c69c603373": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_52fbe169bcef422fb98fa23fcde97845", "style": "IPY_MODEL_072212cf320e4343a41e50650a2972a7", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "52fbe169bcef422fb98fa23fcde97845": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "072212cf320e4343a41e50650a2972a7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e36271fcf75144a8aa8c7e38c45fd436": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5847819749244fbc8d4d9db9042c0ba8", "style": "IPY_MODEL_ecb99c4eb08c4fc0a994b9ad8cef73ee", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5847819749244fbc8d4d9db9042c0ba8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ecb99c4eb08c4fc0a994b9ad8cef73ee": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e69cf450b758423d97f1b844a04ff5af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e1f0c0c4a27443d6a75962d8777ccfdd", "style": "IPY_MODEL_9616468d3c7843c9b3585e61b4e229f6", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d4f37_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4f37_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4f37_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4f37_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d4f37_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d4f37_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_d4f37_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d4f37_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_d4f37_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.55e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e1f0c0c4a27443d6a75962d8777ccfdd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9616468d3c7843c9b3585e61b4e229f6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f76686b188ae4a278ff11e07380f49d8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_df56509ee36742e6bbbf0ddf732c9c25", "IPY_MODEL_35dd3b4fb8c8477f959b00e187720d55", "IPY_MODEL_558799346a524ec590d98608177ba7f8", "IPY_MODEL_1785335882044134823b7989920c38a3"], "layout": "IPY_MODEL_e6eab2cf31394b7ea809c7236cfc6713"}}, "df56509ee36742e6bbbf0ddf732c9c25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7807c080797848009e46fe65b1f655cd", "style": "IPY_MODEL_38282b29eb4f432488a9cf4c8a70e9d5", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7807c080797848009e46fe65b1f655cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38282b29eb4f432488a9cf4c8a70e9d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "35dd3b4fb8c8477f959b00e187720d55": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3b90432c062242ef9e3c452153236cd0", "style": "IPY_MODEL_df32ecca26d94802a3606a4bc1db2884", "value": "<div>Average model inference time for one sample (in seconds): 1.55e-06</div>"}}, "3b90432c062242ef9e3c452153236cd0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df32ecca26d94802a3606a4bc1db2884": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "558799346a524ec590d98608177ba7f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a2a9a7d47834229888e4c8c1bae8fa9", "style": "IPY_MODEL_f9126e8099bc447991f54b34dc6bb7fa", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 8626. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "0a2a9a7d47834229888e4c8c1bae8fa9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9126e8099bc447991f54b34dc6bb7fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1785335882044134823b7989920c38a3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd58aede4a8c4f77b0f42bd156365882", "style": "IPY_MODEL_e8ea6270f02c4f4d8a74cf7ef1b2af46", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "fd58aede4a8c4f77b0f42bd156365882": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e8ea6270f02c4f4d8a74cf7ef1b2af46": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e6eab2cf31394b7ea809c7236cfc6713": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c762bd3f8634d9ca3ad28d1db4a6230": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee024fecb9a54ae986b2ce9180859159": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_76133935631a4204afbd502401e49076", "IPY_MODEL_513ffd6f531b4e088c2bd7d4af908d1c", "IPY_MODEL_333efd1986c54416950c7c9527ada82d", "IPY_MODEL_643a813a2d054f43a7ed3fb3ca2c3ffe"], "layout": "IPY_MODEL_84cf43110d0c4110939c0a5c849eb60b"}}, "76133935631a4204afbd502401e49076": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a3726d9171743d2aa2da5bebe8070c1", "style": "IPY_MODEL_fc4a19d2c489429aa7a2403b5d430250", "value": "<h4 id=\"ModelInferenceTime-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "3a3726d9171743d2aa2da5bebe8070c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc4a19d2c489429aa7a2403b5d430250": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "513ffd6f531b4e088c2bd7d4af908d1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_782e7b40fa234c9481d3130ab38d6b2f", "style": "IPY_MODEL_8b6f1049bd874940b1e09fb378c692b3", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "782e7b40fa234c9481d3130ab38d6b2f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b6f1049bd874940b1e09fb378c692b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "333efd1986c54416950c7c9527ada82d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b41bb839836142b5b1fd21043ddf2829", "style": "IPY_MODEL_213751d057df4845b55fe3e7abcdc7ab", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f8c5d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f8c5d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f8c5d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f8c5d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f8c5d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f8c5d_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f8c5d_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f8c5d_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_f8c5d_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.46e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b41bb839836142b5b1fd21043ddf2829": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "213751d057df4845b55fe3e7abcdc7ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "643a813a2d054f43a7ed3fb3ca2c3ffe": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d6c8bf347fc742fb82960f99d6008b3f", "IPY_MODEL_2ff283d3ae614bf58b15adf01a57b532", "IPY_MODEL_89f2e8fd29a442f883d46c1cd39a550b", "IPY_MODEL_45443bb91a894b2d8d67e39604f1e140"], "layout": "IPY_MODEL_9798d127ca0f4affbf2e4d2341a11231"}}, "d6c8bf347fc742fb82960f99d6008b3f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c493d674ec243c4bfb2e73fa7018492", "style": "IPY_MODEL_2b23c9de50cf4353a4e71037b56c7424", "value": "<h5><b>Additional Outputs</b></h5>"}}, "7c493d674ec243c4bfb2e73fa7018492": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b23c9de50cf4353a4e71037b56c7424": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2ff283d3ae614bf58b15adf01a57b532": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7eb7478fab534369a0f34556e76a58d0", "style": "IPY_MODEL_9ddf1743c81e4df686a16a8539a358fc", "value": "<div>Average model inference time for one sample (in seconds): 1.46e-06</div>"}}, "7eb7478fab534369a0f34556e76a58d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ddf1743c81e4df686a16a8539a358fc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "89f2e8fd29a442f883d46c1cd39a550b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dfc94cfb4aa24eeba79264b55401dd4d", "style": "IPY_MODEL_9887869f40a54674b5a19f043cfbe6bf", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 2724. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "dfc94cfb4aa24eeba79264b55401dd4d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9887869f40a54674b5a19f043cfbe6bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45443bb91a894b2d8d67e39604f1e140": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e045584240354f02901cbfef346862a7", "style": "IPY_MODEL_24ad8fcc9ba3409f93a718a24461ce52", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-CNNO3\" style=\"font-size: 14px;\">Go to top</a>"}}, "e045584240354f02901cbfef346862a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "24ad8fcc9ba3409f93a718a24461ce52": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9798d127ca0f4affbf2e4d2341a11231": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "84cf43110d0c4110939c0a5c849eb60b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b9201f538d9e454eae88004303e40a87": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b564eca2b394021a042c01353d8fa23": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5d07fc5c55d43baa1df6ab64b512a68": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1263a98cf38e470092089de6f0d21daf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4352d00f8aed45d6860ecc77c63b6f17", "IPY_MODEL_679e0a5ebb8b4b00bb0b74a5212bcb0d"], "layout": "IPY_MODEL_b610ced3622048bf89601d2cb02465ae"}}, "4352d00f8aed45d6860ecc77c63b6f17": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e9887850d43549c4a9bda2ace040c649", "style": "IPY_MODEL_aad193b0459f4e178a98a6565a48943a", "value": "<span id=\"summary_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\"></span>"}}, "e9887850d43549c4a9bda2ace040c649": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aad193b0459f4e178a98a6565a48943a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "679e0a5ebb8b4b00bb0b74a5212bcb0d": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_83cf518ee5654a9cacb32fc9bdb008f3"], "layout": "IPY_MODEL_9b21fd1961474abdbbc7d97b631a844c", "selected_index": null}}, "83cf518ee5654a9cacb32fc9bdb008f3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4e36663b68dd4263acb34695d7f388a9", "IPY_MODEL_6406e8b9a923491585224f8f2248edbb", "IPY_MODEL_46ed8a5bcd9e4d5fb26d2b2fed10a68b", "IPY_MODEL_81035b5d44c5416aa4b69c08ac6dbdeb", "IPY_MODEL_c586d6af30b24a2496c4faa6ea97fcd2", "IPY_MODEL_81035b5d44c5416aa4b69c08ac6dbdeb", "IPY_MODEL_fd693af616944967aa8a378391a2d54c", "IPY_MODEL_81035b5d44c5416aa4b69c08ac6dbdeb", "IPY_MODEL_2af068e2430c4458878d0e779a23779c"], "layout": "IPY_MODEL_65ba5db63efb4810b45a68f670ffb9de"}}, "4e36663b68dd4263acb34695d7f388a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_812d3518e73c418fb1c151b8a28b66d7", "style": "IPY_MODEL_bdd40a6970ac4a648569a01fed89abad", "value": "<style type=\"text/css\">\n#T_5e738_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e738_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e738_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e738_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e738_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5e738_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5e738_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrixReport-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\">Confusion Matrix Report - Train Dataset</a></td>\n      <td id=\"T_5e738_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_5e738_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrixReport-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\">Confusion Matrix Report - Test Dataset</a></td>\n      <td id=\"T_5e738_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_5e738_row2_col0\" class=\"data row2 col0\" ><a href=\"#CalibrationMetric-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\">Calibration Metric - Train Dataset</a></td>\n      <td id=\"T_5e738_row2_col1\" class=\"data row2 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_5e738_row3_col0\" class=\"data row3 col0\" ><a href=\"#CalibrationMetric-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\">Calibration Metric - Test Dataset</a></td>\n      <td id=\"T_5e738_row3_col1\" class=\"data row3 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "812d3518e73c418fb1c151b8a28b66d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bdd40a6970ac4a648569a01fed89abad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6406e8b9a923491585224f8f2248edbb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8b8037eb22d744399df365de98cbb5af", "style": "IPY_MODEL_6d38381755134883a6f749b083eda032", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8b8037eb22d744399df365de98cbb5af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d38381755134883a6f749b083eda032": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "46ed8a5bcd9e4d5fb26d2b2fed10a68b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_24e6012fcb5a44b1b60bbf5ff2409924", "IPY_MODEL_6b7252965ff8446fad8f9ed6dd7760d8", "IPY_MODEL_414194e0dd8c43f89c1eb9c964b16604"], "layout": "IPY_MODEL_89fb2b16a24b4142b5b8b8706201557d"}}, "24e6012fcb5a44b1b60bbf5ff2409924": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4282c3f14b0d4f91be3e1bda51228036", "style": "IPY_MODEL_aa6a67c2760e4790befd8925e475b12b", "value": "<h4 id=\"ConfusionMatrixReport-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\"><b>Confusion Matrix Report - Train Dataset</b></h4>"}}, "4282c3f14b0d4f91be3e1bda51228036": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa6a67c2760e4790befd8925e475b12b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6b7252965ff8446fad8f9ed6dd7760d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_575c10d4cb8c42e09394eab7c16c863e", "style": "IPY_MODEL_ed0cd14c5b9248e8b5502986fd1154a1", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "575c10d4cb8c42e09394eab7c16c863e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed0cd14c5b9248e8b5502986fd1154a1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "414194e0dd8c43f89c1eb9c964b16604": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a2a3b155d0bf4add895f85184436eef2", "IPY_MODEL_25f8fc94eeeb4cc0a2f187cf55f7c1ef", "IPY_MODEL_df03cc152e214a4cb88d3cd3a534c830", "IPY_MODEL_fe2728792f774820893fddc56953bfde"], "layout": "IPY_MODEL_e3bb6751f51947ca895470e966ef4e36"}}, "a2a3b155d0bf4add895f85184436eef2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1a1654a9b48a42dab5348c44c66b8dab", "style": "IPY_MODEL_af6bfe1825a54e559536e4304e506c69", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1a1654a9b48a42dab5348c44c66b8dab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "af6bfe1825a54e559536e4304e506c69": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "25f8fc94eeeb4cc0a2f187cf55f7c1ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_738ba18afa654fc69ad98f2e8abfc613", "style": "IPY_MODEL_ba3186f618134895b85f972589d2b28f", "value": "<div>The overall accuracy of your model is: 100.0%.</div>"}}, "738ba18afa654fc69ad98f2e8abfc613": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ba3186f618134895b85f972589d2b28f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "df03cc152e214a4cb88d3cd3a534c830": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[96.92, 100.0, 100.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [3.08, 100.0, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[8360, 0], [0, 266]], "texttemplate": "%{text}", "x": ["0", "1"], "xgap": 1, "y": ["0", "1"], "ygap": 1, "z": [["100", "0"], ["0", "100"]], "type": "heatmap", "uid": "497feeaa-fe69-4a6a-9a66-bb33d6ed17d3"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "fe2728792f774820893fddc56953bfde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6c33ee35db81464c84f2d0d03b9ca434", "style": "IPY_MODEL_f2e641f257cc43398de2f0e451e677a3", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\" style=\"font-size: 14px;\">Go to top</a>"}}, "6c33ee35db81464c84f2d0d03b9ca434": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2e641f257cc43398de2f0e451e677a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e3bb6751f51947ca895470e966ef4e36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "89fb2b16a24b4142b5b8b8706201557d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81035b5d44c5416aa4b69c08ac6dbdeb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3687c0178593434899ad27a82ac6892a", "style": "IPY_MODEL_94843e56a1df44968019096c0319707e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "3687c0178593434899ad27a82ac6892a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "94843e56a1df44968019096c0319707e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c586d6af30b24a2496c4faa6ea97fcd2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_a26e48f14d2141ff94aa5e49eea8fdcd", "IPY_MODEL_8129cb97cc144d2f97f08a7013901b57", "IPY_MODEL_997877a83845474a9050b5d783b3ab58"], "layout": "IPY_MODEL_6f5de2ef9fe44fae99e261f72b163165"}}, "a26e48f14d2141ff94aa5e49eea8fdcd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c47586bda734f6ca852367b73ea5dd5", "style": "IPY_MODEL_147587dfa3a443c6a191115562483f4e", "value": "<h4 id=\"ConfusionMatrixReport-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\"><b>Confusion Matrix Report - Test Dataset</b></h4>"}}, "4c47586bda734f6ca852367b73ea5dd5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "147587dfa3a443c6a191115562483f4e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8129cb97cc144d2f97f08a7013901b57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c5c0e0a44439422f859a370567f633b9", "style": "IPY_MODEL_0481b00217cc4f00bad375e15f8f7d0a", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c5c0e0a44439422f859a370567f633b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0481b00217cc4f00bad375e15f8f7d0a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "997877a83845474a9050b5d783b3ab58": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d28a510a586d40c8be7a67b11d6ee9d7", "IPY_MODEL_b2c3a08646ff452695598ac77b555429", "IPY_MODEL_58ab572ee7c244e7baa8b53b159f327f", "IPY_MODEL_2b4a119610144342ac7a677631d378b3"], "layout": "IPY_MODEL_c06167ee818b43ceb4174ae7b08f15d9"}}, "d28a510a586d40c8be7a67b11d6ee9d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0fd7f3d05863489a9f8b3705e5fdb617", "style": "IPY_MODEL_d5c7b2c15569436f89a2ec07d98559e4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "0fd7f3d05863489a9f8b3705e5fdb617": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5c7b2c15569436f89a2ec07d98559e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b2c3a08646ff452695598ac77b555429": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c5aa52e048b4639b0495b74f5e56b17", "style": "IPY_MODEL_37151a4deaa046468456ea253c75abcb", "value": "<div>The overall accuracy of your model is: 91.9%.<br>Best accuracy achieved on samples with <b>0</b> label (99.28%).<br>Worst accuracy achieved on samples with <b>1</b> label (84.52%).</div>"}}, "4c5aa52e048b4639b0495b74f5e56b17": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37151a4deaa046468456ea253c75abcb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58ab572ee7c244e7baa8b53b159f327f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[96.22, 99.28, 99.51], [0.7, 0.72, 0.72]], [[0.48, 15.48, 14.44], [2.61, 84.52, 78.89]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[2621, 19], [13, 71]], "texttemplate": "%{text}", "x": ["0", "1"], "xgap": 1, "y": ["0", "1"], "ygap": 1, "z": [["99.28", "0.72"], ["15.48", "84.52"]], "type": "heatmap", "uid": "84255077-69e6-415d-9dea-cbeeb692aa5b"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": 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"_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "2b4a119610144342ac7a677631d378b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9426678526454f9b98f6603dab13172c", "style": "IPY_MODEL_8e7453718d054d929e2fef559906e7c4", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\" style=\"font-size: 14px;\">Go to top</a>"}}, "9426678526454f9b98f6603dab13172c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8e7453718d054d929e2fef559906e7c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c06167ee818b43ceb4174ae7b08f15d9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f5de2ef9fe44fae99e261f72b163165": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fd693af616944967aa8a378391a2d54c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_1e53855cd63b4858b572be9dc04f3be2", "IPY_MODEL_6e44bb83c5de4b94bf931eac14b3d16b", "IPY_MODEL_5929ec9311d6440682106196ff37245d"], "layout": "IPY_MODEL_be43253c4ec143809d71d5969dc31115"}}, "1e53855cd63b4858b572be9dc04f3be2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9db9eb708df41bcbcd3717efe185cb2", "style": "IPY_MODEL_4a13953522494bd6b60a75792e6d3bae", "value": "<h4 id=\"CalibrationMetric-TrainDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\"><b>Calibration Metric - Train Dataset</b></h4>"}}, "b9db9eb708df41bcbcd3717efe185cb2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4a13953522494bd6b60a75792e6d3bae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6e44bb83c5de4b94bf931eac14b3d16b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a992ec5b40048c2bcc1bff804280328", "style": "IPY_MODEL_d49ec07816f04af3b82f08117f8780c3", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4a992ec5b40048c2bcc1bff804280328": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d49ec07816f04af3b82f08117f8780c3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5929ec9311d6440682106196ff37245d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dad3a43f24ba4b1ebe3ea6ae4125f298", "IPY_MODEL_459dac753eea4d7f8badaa8176f5017d", "IPY_MODEL_c700a7309f7a40a3ae390ed93f968f4f", "IPY_MODEL_552795d884824c1b89733c742b089811", "IPY_MODEL_8a1551a0a3124be88e3077fb7223ebb9"], "layout": "IPY_MODEL_59aaca7abe0f4ca4a9fc82c9f8780006"}}, "dad3a43f24ba4b1ebe3ea6ae4125f298": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c44e2edb98a48eeba6576e1f1501493", "style": "IPY_MODEL_fb6d7a269cb0463786768e673af372f6", "value": "<h5><b>Additional Outputs</b></h5>"}}, "5c44e2edb98a48eeba6576e1f1501493": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb6d7a269cb0463786768e673af372f6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "459dac753eea4d7f8badaa8176f5017d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3be9afddeb5a48fe9271ba4ab3344c2b", "style": "IPY_MODEL_565b952565ce45c8a7576a367466c915", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.</div>"}}, "3be9afddeb5a48fe9271ba4ab3344c2b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "565b952565ce45c8a7576a367466c915": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c700a7309f7a40a3ae390ed93f968f4f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "1b978157-be8a-4e75-a1c6-19658502b99c"}, {"mode": "lines+markers", "name": "(brier:   0.0000)", "x": {"dtype": "float64", "shape": [2]}, "y": {"dtype": "float64", "shape": [2]}, "type": "scatter", "uid": 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"zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Calibration plots (reliability curve)"}, "xaxis": {"title": {"text": "Mean predicted value"}}, "yaxis": {"title": {"text": "Fraction of positives"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}]}, "552795d884824c1b89733c742b089811": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a849c40c0f8c4e43b80f3a4d9198c2be", "style": "IPY_MODEL_e1720ec777ac4438a3878e3e0fb391c1", "value": "<div>The Brier score metric may be used to assess how well a classifier is calibrated. For more info, please visit https://en.wikipedia.org/wiki/Brier_score</div>"}}, "a849c40c0f8c4e43b80f3a4d9198c2be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e1720ec777ac4438a3878e3e0fb391c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8a1551a0a3124be88e3077fb7223ebb9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b6edebcb7d704aa6926b069435e53c90", "style": "IPY_MODEL_f7b015926e734240a6b27cd87b95767e", "value": "<br><a href=\"#summary_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\" style=\"font-size: 14px;\">Go to top</a>"}}, "b6edebcb7d704aa6926b069435e53c90": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7b015926e734240a6b27cd87b95767e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59aaca7abe0f4ca4a9fc82c9f8780006": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be43253c4ec143809d71d5969dc31115": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2af068e2430c4458878d0e779a23779c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_107501566a5e414580bf7d9c2a2992fa", "IPY_MODEL_3f4657187d4e47c29df07fbc5606a947", "IPY_MODEL_02ae8a41153d4d92a9a63764f7df1887"], "layout": "IPY_MODEL_d82fc2426ab94b448c14ccb23f25392d"}}, "107501566a5e414580bf7d9c2a2992fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c82317de483f45f19884a3d2b7ea2b7e", "style": "IPY_MODEL_b28a3fb94c37454d9e38272b1122a07f", "value": "<h4 id=\"CalibrationMetric-TestDataset_F1BKDOV1MJS5KIXYX460A1DMH-section-KL9PQ\"><b>Calibration Metric - Test Dataset</b></h4>"}}, "c82317de483f45f19884a3d2b7ea2b7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b28a3fb94c37454d9e38272b1122a07f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3f4657187d4e47c29df07fbc5606a947": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_680a9beb16d7467f9889be8eeca59297", "style": "IPY_MODEL_39cece7f3d914873a79f09e6d29cf010", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "680a9beb16d7467f9889be8eeca59297": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39cece7f3d914873a79f09e6d29cf010": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02ae8a41153d4d92a9a63764f7df1887": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0207738f607c49098921b0eea24621e0", "IPY_MODEL_8ddd73efa08d4eb08282cac2f6d2c709", "IPY_MODEL_7bbda52da8b147528230e91781101331", "IPY_MODEL_302bd8f246d64bb19e34ca0fd68d649e", "IPY_MODEL_0995b630a64c45ab98c4d00151a5d661"], "layout": "IPY_MODEL_e7d55fdd0f6a4da1b3b2f2dd0720621a"}}, "0207738f607c49098921b0eea24621e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ebc384ab75f447b0ba14798459f28f49", "style": "IPY_MODEL_8140e7bc974e4c88a320c1ef3b7d0ba8", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ebc384ab75f447b0ba14798459f28f49": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8140e7bc974e4c88a320c1ef3b7d0ba8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8ddd73efa08d4eb08282cac2f6d2c709": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_84cc8d0357b04d0793aff6a2e1b3d5a6", "style": "IPY_MODEL_dfb7309c4dc04770b407c20fa1038f6d", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.</div>"}}, "84cc8d0357b04d0793aff6a2e1b3d5a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dfb7309c4dc04770b407c20fa1038f6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7bbda52da8b147528230e91781101331": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "1419d270-5c39-4348-bc68-ce8a62651cbb"}, {"mode": "lines+markers", "name": "(brier:   0.0117)", "x": {"dtype": "float64", "shape": [2]}, "y": {"dtype": "float64", "shape": [2]}, "type": "scatter", "uid": 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Try increasing n_samples or supply additional features.</td>\n    </tr>\n    <tr>\n      <td id=\"T_228d3_row1_col0\" class=\"data row1 col0\" >Regression Error Distribution - Train Dataset</td>\n      <td id=\"T_228d3_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_228d3_row2_col0\" class=\"data row2 col0\" >Regression Error Distribution - Test Dataset</td>\n      <td id=\"T_228d3_row2_col1\" class=\"data row2 col1\" >Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_228d3_row3_col0\" class=\"data row3 col0\" >Boosting Overfit</td>\n      <td id=\"T_228d3_row3_col1\" class=\"data row3 col1\" >Check is relevant for Boosting models of type ('AdaBoostClassifier', 'GradientBoostingClassifier', 'LGBMClassifier', 'XGBClassifier', 'CatBoostClassifier', 'AdaBoostRegressor', 'GradientBoostingRegressor', 'LGBMRegressor', 'XGBRegressor', 'CatBoostRegressor'), but received model of type DecisionTreeClassifier</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1cd70f2cfd204fae90524e02428be108": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5b41731498f94ad4b35edee8e1180ac7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e3880764abd947ce927ce48d2976a6f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "859679fb6d294238bff3480e11f6908c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9240af43eeda47f89bdd3fdbed9bfe2a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "04b74a0a5f9b4790b385725d1a900dde": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
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.. GENERATED FROM PYTHON SOURCE LINES 371-376

Boosting our model!
===================
To try and solve the overfitting issue let's try and throw at a problem an ensemble
model that has a bit more resilience to overfitting than a decision tree: a
gradient-boosted ensemble of them!

.. GENERATED FROM PYTHON SOURCE LINES 376-383

.. code-block:: default


    from sklearn.ensemble import GradientBoostingClassifier

    model = GradientBoostingClassifier(n_estimators=250, random_state=SEED, max_depth=20, subsample=0.8 , loss='exponential')
    model.fit(train_X, train_y)
    msuite.run(model=model, train_dataset=ds_train, test_dataset=ds_test)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Model Evaluation Suite:
    |           | 0/11 [Time: 00:00]
    Model Evaluation Suite:
    |█          | 1/11 [Time: 00:00, Check=Train Test Performance]
    Model Evaluation Suite:
    |██         | 2/11 [Time: 00:01, Check=Roc Report]            
    Model Evaluation Suite:
    |███        | 3/11 [Time: 00:01, Check=Confusion Matrix Report]
    Model Evaluation Suite:
    |████       | 4/11 [Time: 00:01, Check=Prediction Drift]       
    Model Evaluation Suite:
    |█████      | 5/11 [Time: 00:01, Check=Simple Model Comparison]
    Model Evaluation Suite:
    |██████     | 6/11 [Time: 01:02, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |███████    | 7/11 [Time: 01:02, Check=Calibration Score]        
    Model Evaluation Suite:
    |█████████  | 9/11 [Time: 01:02, Check=Unused Features]  
    Model Evaluation Suite:
    |██████████ | 10/11 [Time: 01:05, Check=Boosting Overfit]
                                                                                 

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id=\"T_c7209_row2_col2\" class=\"data row2 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_c7209_row2_col3\" class=\"data row2 col3\" >Found 8 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "86efd266508249b4af2891bbd60c2dcb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e2eb5ac6bb048e6a4d0fde83763f4af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ace7311e55b5420bb96f2b782f2356d0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b0155c97117049ee8172ab65a514f430", "style": "IPY_MODEL_ab12dcbac89d46e5b9221137a500759b", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b0155c97117049ee8172ab65a514f430": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab12dcbac89d46e5b9221137a500759b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "24ce812c5f82465daed882df9c858457": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_feed20f9167f4953b8dfdd502ec8c07d", "IPY_MODEL_7bdebcb6d5b84842a9d9bcf8b6197f26", "IPY_MODEL_fbd02dfb0bbb4c14b8bed94664dd7ae6", "IPY_MODEL_214c6bbe38304060869d253d1364e0f9"], "layout": "IPY_MODEL_ab6be0083a9a46398799e1791405882c"}}, "feed20f9167f4953b8dfdd502ec8c07d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6da454c3b0c84437911634b9e4eae81c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e966bb804ef74995afd46bec35b63997": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbd02dfb0bbb4c14b8bed94664dd7ae6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0fff8a6a71ea43388c0d783113a78e00", "style": "IPY_MODEL_e0bb5474340a4d2a826200e74a40c85b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_7ff55_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ff55_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ff55_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ff55_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ff55_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7ff55_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_7ff55_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_7ff55_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_7ff55_row0_col2\" class=\"data row0 col2\" >2 scores failed. Found max degradation of 14.29% for metric Recall and class 1.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0fff8a6a71ea43388c0d783113a78e00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e0bb5474340a4d2a826200e74a40c85b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "214c6bbe38304060869d253d1364e0f9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c4bb07878e5646fe9a3bc12bb48e8b7a", "IPY_MODEL_4f7ac825b0b541f2accdaaeb369ddfe8", "IPY_MODEL_4bf854c109134cab80511d15b638083c"], "layout": "IPY_MODEL_eb74e32b66e44d099e1beee50bfb94a7"}}, "c4bb07878e5646fe9a3bc12bb48e8b7a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "75515026-911c-4a89-ada0-eb9a03d53e78"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": [0, 1], "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "histogram", "uid": "57cb1acb-65c4-49b2-a462-734f374827f7"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": [0, 1], "xaxis": "x3", 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"model_module_version": "1.5.0", "state": {}}, "eb74e32b66e44d099e1beee50bfb94a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab6be0083a9a46398799e1791405882c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf3ebe8a8d9e49efa928859a251d2afc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5d8b669b4fa4f0ebc19e07a8cc20780", "style": "IPY_MODEL_32a8cb0eb6174531bae68a24739070bb", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a5d8b669b4fa4f0ebc19e07a8cc20780": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "32a8cb0eb6174531bae68a24739070bb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "63dccb68baa94cd78fef82c8a0be6e98": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_abae740c43d046f8a58ebfc87d1ac31c", "IPY_MODEL_82f4327757a647f48693506b8a2e7537", "IPY_MODEL_da7f9d9db3a94a339025092d4fb47dd2", "IPY_MODEL_1a2e6f7e93614e18bcc9c9a1b6d3f5f4"], "layout": "IPY_MODEL_4618f31c0d7b4899b92cad39c8c0c2e7"}}, "abae740c43d046f8a58ebfc87d1ac31c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a29dce91aabb42949af17a907edc1c29", "style": "IPY_MODEL_1498941cdb704e85b974c5b9fb78163b", "value": "<h4 id=\"UnusedFeatures-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-5CLOQ\"><b>Unused Features - Train Dataset</b></h4>"}}, "a29dce91aabb42949af17a907edc1c29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1498941cdb704e85b974c5b9fb78163b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "82f4327757a647f48693506b8a2e7537": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21ab6247c4454f7188aefbd65b146eb5", "style": "IPY_MODEL_7cd129f811244fcd94a89f79119a766c", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "21ab6247c4454f7188aefbd65b146eb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7cd129f811244fcd94a89f79119a766c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da7f9d9db3a94a339025092d4fb47dd2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_efe9b3e9adc740908e1ce2ed9d421b8c", "style": "IPY_MODEL_e89b29de18224118bb0da23883b99d58", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_2cd1c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2cd1c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2cd1c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2cd1c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2cd1c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2cd1c_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_2cd1c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_2cd1c_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_2cd1c_row0_col2\" class=\"data row0 col2\" >Found 8 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "efe9b3e9adc740908e1ce2ed9d421b8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e89b29de18224118bb0da23883b99d58": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1a2e6f7e93614e18bcc9c9a1b6d3f5f4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d4e9588c89ea4c8aa48189c737ba49a6", "IPY_MODEL_9314672a4f214af88477c628d5d52930", "IPY_MODEL_9e0aabc003f2430783d9a9ffbea5af38", "IPY_MODEL_79ee8ff8871b49a8af1052cef714851b"], "layout": "IPY_MODEL_7446bb01812e46e695251794752aac1e"}}, "d4e9588c89ea4c8aa48189c737ba49a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_386325d8b5d04d9f8e57cfe507e5d294", "style": "IPY_MODEL_c6976d8f39224fa79f4fc13fd3469525", "value": "<h5><b>Additional Outputs</b></h5>"}}, "386325d8b5d04d9f8e57cfe507e5d294": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6976d8f39224fa79f4fc13fd3469525": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9314672a4f214af88477c628d5d52930": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3282707d98bd4556bb710a56585d741c", "style": "IPY_MODEL_35ec990bdd6c4957862745fba5a1d3ab", "value": "<div>Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters</div>"}}, "3282707d98bd4556bb710a56585d741c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "35ec990bdd6c4957862745fba5a1d3ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e0aabc003f2430783d9a9ffbea5af38": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", 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[0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Unused features compared to top important features"}, "yaxis": {"autorange": "reversed"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": "gYV4Dk5zSkDB+N37rYgoQAj2D7HDXxhAKhM6g0CMF0CA0CYtcc4GQJXhGay6K9k/gxNgDqj85z8O8e3KUT7ZPwAAAAAAAAAAYmjzwu/Duz/HpUJDqHfSP/eDdBYlkdg/GrxGOn4eyT8="}, {"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "RvnFDQbtCEDMOx8EPKAHQH9S5B06ogtAL8ROOFEqEEB4OsJ1vRbzP0PZ6ytHyRNAMRXGXAIQEECPVUwwsHUOQN9fWAB8Ag1AGRfq87NdDEAn9MZe0dwLQCqYzN/aUgtAs1dNr/9HC0A="}]}, "79ee8ff8871b49a8af1052cef714851b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3fa984a256f14651a70904ece2a013ff", "style": "IPY_MODEL_f5d3bdf032214f038b46f6ab4c6ed5a8", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-5CLOQ\" style=\"font-size: 14px;\">Go to top</a>"}}, "3fa984a256f14651a70904ece2a013ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f5d3bdf032214f038b46f6ab4c6ed5a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7446bb01812e46e695251794752aac1e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4618f31c0d7b4899b92cad39c8c0c2e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e593fb41d064dd1ae85152fa2514ce2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_877b3ff6f46b4caab069a18b2f591af0", "IPY_MODEL_f8c20cbb4b094ac6a00ea82766ab5608", "IPY_MODEL_5c30ac93fece4117ba9967c3c57bc1e5", "IPY_MODEL_8945ad66fac443e1944c9727d98f114b"], "layout": "IPY_MODEL_73f29724d44546eb8d35ee7fe0560717"}}, "877b3ff6f46b4caab069a18b2f591af0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7dae06a4e16643059231752ac1b8807e", "style": "IPY_MODEL_d16ed28e46b540dcaca539fb17a8c3b5", "value": "<h4 id=\"UnusedFeatures-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-5CLOQ\"><b>Unused Features - Test Dataset</b></h4>"}}, "7dae06a4e16643059231752ac1b8807e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d16ed28e46b540dcaca539fb17a8c3b5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8c20cbb4b094ac6a00ea82766ab5608": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_069bfe9120f54ecca017a88c52cf67a8", "style": "IPY_MODEL_36c5bef6bb7344a7a8833215937c2094", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "069bfe9120f54ecca017a88c52cf67a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36c5bef6bb7344a7a8833215937c2094": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c30ac93fece4117ba9967c3c57bc1e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_005eb67edeed45a196ce82cc32c22918", "style": "IPY_MODEL_5376794b212f4495a2f7c111b94379b0", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e262d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e262d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e262d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e262d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e262d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e262d_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e262d_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_e262d_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_e262d_row0_col2\" class=\"data row0 col2\" >Found 8 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "005eb67edeed45a196ce82cc32c22918": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5376794b212f4495a2f7c111b94379b0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8945ad66fac443e1944c9727d98f114b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1ef30bc3a400410ca225dab54a576f76", "IPY_MODEL_67ec309e0b5c4a13801d64c14f1b31ee", "IPY_MODEL_91d9186779c64b579f7a222c84ba0d8a", "IPY_MODEL_773364a15b2e4e80b6b5e34c9bf2c264"], "layout": "IPY_MODEL_236642b76fdc4d18b79beeea488310dc"}}, "1ef30bc3a400410ca225dab54a576f76": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3ccca4a4de4a41acacc0b826cd51f510", "style": "IPY_MODEL_8dc10c829777407484bbc4d371eb913f", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3ccca4a4de4a41acacc0b826cd51f510": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8dc10c829777407484bbc4d371eb913f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "67ec309e0b5c4a13801d64c14f1b31ee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cfca71c0af01473988d17395f2383477", "style": "IPY_MODEL_b41629b9df68457c8650132b82140897", "value": "<div>Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters</div>"}}, "cfca71c0af01473988d17395f2383477": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b41629b9df68457c8650132b82140897": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "91d9186779c64b579f7a222c84ba0d8a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "indianred"}, "name": "Feature Importance %", "orientation": "h", "x": {"dtype": "float64", "shape": [13]}, "y": ["urlLength", "entropy", "numDigits", "ext_net", "dsr", "ext_info", "ext_country", "ext_html", "ext_other", "num_@", "hasHttps", "hasHttp", "ext_php"], "type": "bar", "uid": "7d4debe6-c48d-486d-8d60-5620059850cf"}, {"marker": {"color": "lightsalmon"}, "name": "Feature Variance %", "orientation": "h", "x": {"dtype": "float64", "shape": [13]}, "y": ["urlLength", "entropy", "numDigits", "ext_net", "dsr", "ext_info", "ext_country", "ext_html", "ext_other", "num_@", "hasHttps", "hasHttp", "ext_php"], "type": "bar", "uid": "701ce8a1-2e34-4f77-a11e-5d285af9ae1f"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Last shown significant feature", "x": 1, "xanchor": "right", "xref": "x domain", "y": 4.5, "yanchor": "bottom", "yref": "y"}], "height": 500, "shapes": [{"line": {"color": "green", "dash": "dash", "width": 2}, "type": "line", "x0": 0, "x1": 1, "xref": "x domain", "y0": 4.5, "y1": 4.5, "yref": 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"IPY_MODEL_a789f8c2584b4530af2f9e769de05dda", "IPY_MODEL_587aee3beb4748b1a8e75e86eeefa734", "IPY_MODEL_a789f8c2584b4530af2f9e769de05dda", "IPY_MODEL_6841203e928d45bfb4f2b84d05861b86", "IPY_MODEL_a789f8c2584b4530af2f9e769de05dda", "IPY_MODEL_c913baa21b884d5186320c31f653fc4c", "IPY_MODEL_a789f8c2584b4530af2f9e769de05dda", "IPY_MODEL_8e9c1d2cd09e4bd7be7d4663c243fefb"], "layout": "IPY_MODEL_dd37056f06a9472ea243fad636bd4281"}}, "bdbf3012d340404bb5445057a9221958": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a2364915b1fc4825ba8ecaf3d58fd569", "style": "IPY_MODEL_a411618066bb408db3ca2d91be326122", "value": "<style type=\"text/css\">\n#T_baaf1_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baaf1_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baaf1_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baaf1_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_baaf1_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_baaf1_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Check</th>\n      <th class=\"col_heading level0 col2\" >Condition</th>\n      <th class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_baaf1_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row0_col1\" class=\"data row0 col1\" ><a href=\"#ROCReport-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">ROC Report - Train Dataset</a></td>\n      <td id=\"T_baaf1_row0_col2\" class=\"data row0 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_baaf1_row0_col3\" class=\"data row0 col3\" >All classes passed, minimum AUC found is 1 for class 1</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row1_col1\" class=\"data row1 col1\" ><a href=\"#ROCReport-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">ROC Report - Test Dataset</a></td>\n      <td id=\"T_baaf1_row1_col2\" class=\"data row1 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_baaf1_row1_col3\" class=\"data row1 col3\" >All classes passed, minimum AUC found is 0.98 for class 1</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row2_col1\" class=\"data row2 col1\" ><a href=\"#PredictionDrift_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">Prediction Drift</a></td>\n      <td id=\"T_baaf1_row2_col2\" class=\"data row2 col2\" >Prediction drift score < 0.15</td>\n      <td id=\"T_baaf1_row2_col3\" class=\"data row2 col3\" >Found model prediction Kolmogorov-Smirnov drift score of 0.05</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row3_col1\" class=\"data row3 col1\" ><a href=\"#SimpleModelComparison_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">Simple Model Comparison</a></td>\n      <td id=\"T_baaf1_row3_col2\" class=\"data row3 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_baaf1_row3_col3\" class=\"data row3 col3\" >All classes passed, average gain for metrics: {'F1': '82.69%'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row4_col1\" class=\"data row4 col1\" ><a href=\"#WeakSegmentsPerformance-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">Weak Segments Performance - Test Dataset</a></td>\n      <td id=\"T_baaf1_row4_col2\" class=\"data row4 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_baaf1_row4_col3\" class=\"data row4 col3\" >Found a segment with accuracy score of 0.957 in comparison to an average score of 0.993 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row5_col1\" class=\"data row5 col1\" ><a href=\"#BoostingOverfit_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">Boosting Overfit</a></td>\n      <td id=\"T_baaf1_row5_col2\" class=\"data row5 col2\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_baaf1_row5_col3\" class=\"data row5 col3\" >Found score decline of -0.11%</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_baaf1_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_baaf1_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 1.878e-05</td>\n    </tr>\n    <tr>\n      <td id=\"T_baaf1_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_baaf1_row7_col1\" class=\"data row7 col1\" ><a href=\"#ModelInferenceTime-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_baaf1_row7_col2\" class=\"data row7 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_baaf1_row7_col3\" class=\"data row7 col3\" >Found average inference time (seconds): 1.899e-05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a2364915b1fc4825ba8ecaf3d58fd569": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a411618066bb408db3ca2d91be326122": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c442b9d7d67445309dacfdca98ebbdb3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c94865638b5a416da48fc115d6076328", "style": "IPY_MODEL_78ff9cd00dde4f88b47c66952adc2295", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c94865638b5a416da48fc115d6076328": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "78ff9cd00dde4f88b47c66952adc2295": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2536097fa2124d738e9ab98cad0c678f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_af8a9e5538a043a9945256fc041c256b", "IPY_MODEL_d411d192e45b404fb2db1d52406d851d", "IPY_MODEL_86be9bcb282d441799d94d4219839afb", "IPY_MODEL_f8c767f9c75c49fdbac59e5993fbeed4"], "layout": "IPY_MODEL_e8ff2ad3e0614a3d930881060a4edb40"}}, "af8a9e5538a043a9945256fc041c256b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82bf9ce176fb4edd9b3d7ec9b8dee3f6", "style": "IPY_MODEL_fc0e8f14f3c04ccc97ef8b05f36f0ec5", "value": "<h4 id=\"ROCReport-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>ROC Report - Train Dataset</b></h4>"}}, "82bf9ce176fb4edd9b3d7ec9b8dee3f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc0e8f14f3c04ccc97ef8b05f36f0ec5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d411d192e45b404fb2db1d52406d851d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_91eedc555b474918b2a2aae5344d151d", "style": "IPY_MODEL_30102fbb84c143e29254741cd34686d1", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "91eedc555b474918b2a2aae5344d151d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30102fbb84c143e29254741cd34686d1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "86be9bcb282d441799d94d4219839afb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6662e985f1e042ba90cfb183f364327c", "style": "IPY_MODEL_30e01f08c9c04b33a05975eee133c413", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a4029_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4029_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4029_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4029_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4029_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a4029_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a4029_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a4029_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_a4029_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 1 for class 1</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6662e985f1e042ba90cfb183f364327c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30e01f08c9c04b33a05975eee133c413": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8c767f9c75c49fdbac59e5993fbeed4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c9d31672789a404c8d3f1e57771dd429", "IPY_MODEL_33880d9d38ff4fc89a93570061dada30", "IPY_MODEL_a8d986de9ecb46b3928aa35f3da68726", "IPY_MODEL_5bc9403a5fd946ebb98a7d8e4ae9b9b0"], "layout": "IPY_MODEL_a1d3cb2dde24422999ac57164fe311b9"}}, "c9d31672789a404c8d3f1e57771dd429": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9cd72d3bbbbf4a7394afc268bc124fde", "style": "IPY_MODEL_928f589009404d60bb8ed9c7b7c4d1f4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9cd72d3bbbbf4a7394afc268bc124fde": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "928f589009404d60bb8ed9c7b7c4d1f4": {"model_name": 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"a8d986de9ecb46b3928aa35f3da68726": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ec4ec6e9c2c845af8c0e453544e06056", "style": "IPY_MODEL_203f1ced72a442abb3838037f98e5640", "value": "<div>The marked points are the optimal probability threshold cut-off points to predict said\n            class. In plain terms, it is optimal to set the prediction rule such that if for some class the predicted\n            probability is above the threshold of that class, then the prediction should be that class.\n            They optimal thresholds are determined using Youden's index defined as sensitivity + specificity - 1.</div>"}}, "ec4ec6e9c2c845af8c0e453544e06056": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "203f1ced72a442abb3838037f98e5640": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5bc9403a5fd946ebb98a7d8e4ae9b9b0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8fd52b9689b94f7aa99f0bd86da152ee", "style": "IPY_MODEL_3a82a0a4e3a34bdfb72db1b7f431e2f6", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "8fd52b9689b94f7aa99f0bd86da152ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a82a0a4e3a34bdfb72db1b7f431e2f6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1d3cb2dde24422999ac57164fe311b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e8ff2ad3e0614a3d930881060a4edb40": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a789f8c2584b4530af2f9e769de05dda": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_380e8fb709b1473a8c4cd918e8555d54", "style": "IPY_MODEL_58b4f0cf37cd49cd811f9ec3143e69cf", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "380e8fb709b1473a8c4cd918e8555d54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58b4f0cf37cd49cd811f9ec3143e69cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c259fbac9e1641ff936d50e1bd135562": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_0e3348a0567442608ca694eead13cf0c", "IPY_MODEL_780eb6d0747d47fb8e05e6c6288cef9c", "IPY_MODEL_e30e7d449676407aa8f38b9ec1cddd99", "IPY_MODEL_cd338c7f90004d279d201f135386d3ce"], "layout": "IPY_MODEL_efffb44843c8498aa0c5ca9ea77970bd"}}, "0e3348a0567442608ca694eead13cf0c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1cddb93fc70a49b281310f893d049d60", "style": "IPY_MODEL_d20fa8933f824ee59c03f14fef0787ac", "value": "<h4 id=\"ROCReport-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>ROC Report - Test Dataset</b></h4>"}}, "1cddb93fc70a49b281310f893d049d60": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d20fa8933f824ee59c03f14fef0787ac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "780eb6d0747d47fb8e05e6c6288cef9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4817b2bb48574479816117a0b8bb6142", "style": "IPY_MODEL_27bef87dbd2946b596f0dc50afcfd2bf", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4817b2bb48574479816117a0b8bb6142": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "27bef87dbd2946b596f0dc50afcfd2bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e30e7d449676407aa8f38b9ec1cddd99": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6cb76deeaa4849949e9addb5ee285677", "style": "IPY_MODEL_64084fe491544306879b0e3449cc87d1", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_05045_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05045_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05045_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05045_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_05045_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_05045_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_05045_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_05045_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_05045_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 0.98 for class 1</td>\n    </tr>\n  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"eba7264b19e441de8aada3e51da1c6c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_76f3b265357c4e389095d1228fcd77a1", "style": "IPY_MODEL_8b47f5ce6f634113837c59a48edf76ef", "value": "<div>The marked points are the optimal probability threshold cut-off points to predict said\n            class. In plain terms, it is optimal to set the prediction rule such that if for some class the predicted\n            probability is above the threshold of that class, then the prediction should be that class.\n            They optimal thresholds are determined using Youden's index defined as sensitivity + specificity - 1.</div>"}}, "76f3b265357c4e389095d1228fcd77a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b47f5ce6f634113837c59a48edf76ef": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "46bb8dac4bb64e7e8aad48e66f6998f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b61ff309af2b4f80927380ca29f2a914", "style": "IPY_MODEL_94c36a4436cb4a9cb60158ba29b35149", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "b61ff309af2b4f80927380ca29f2a914": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "94c36a4436cb4a9cb60158ba29b35149": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6edcaca68d3040808fc07db9de757a26": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "efffb44843c8498aa0c5ca9ea77970bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c92343c541294dea83babee5178550e2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2c328a7659f04ca896a6d9ed46e5e3c8", "IPY_MODEL_fa5ba1d6665f4c00aa0009e28b91d19c", "IPY_MODEL_d45a53329d034cd3b7c95dfb911a5cae", "IPY_MODEL_1920eefde93c43cb8c688cb50ed205dc"], "layout": "IPY_MODEL_aeead085d8204a30ac9676cb5c27677d"}}, "2c328a7659f04ca896a6d9ed46e5e3c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b1bb382e311b48c7b0bc7e3c01be0bd5", "style": "IPY_MODEL_e1acc8b1f65c4a2dae9b89bd86fc8dc5", "value": "<h4 id=\"PredictionDrift_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>Prediction Drift</b></h4>"}}, "b1bb382e311b48c7b0bc7e3c01be0bd5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e1acc8b1f65c4a2dae9b89bd86fc8dc5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa5ba1d6665f4c00aa0009e28b91d19c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d0606f8a661d4eeab6e9106c1c8bbf0e", "style": "IPY_MODEL_3aea01b8aec844eb82323a81f126a8cd", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d0606f8a661d4eeab6e9106c1c8bbf0e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3aea01b8aec844eb82323a81f126a8cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d45a53329d034cd3b7c95dfb911a5cae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f5c06a20f2794a9abd254ea86afbdac1", "style": "IPY_MODEL_a79b714f690d42789e16f3f8e4508871", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_85503_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_85503_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_85503_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_85503_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_85503_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_85503_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_85503_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_85503_row0_col1\" class=\"data row0 col1\" >Prediction drift score < 0.15</td>\n      <td id=\"T_85503_row0_col2\" class=\"data row0 col2\" >Found model prediction Kolmogorov-Smirnov drift score of 0.05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f5c06a20f2794a9abd254ea86afbdac1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a79b714f690d42789e16f3f8e4508871": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1920eefde93c43cb8c688cb50ed205dc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3b8857e04be94395aad9d9e18cc69ac9", "IPY_MODEL_94d119a56f1e40538383824b40ccd52c", "IPY_MODEL_92f93e8f9d934ccbab1fd0997d416cd1", "IPY_MODEL_53366b10ce7b40a6ab26db8019e18757", "IPY_MODEL_3542b815cbfb4e18800c612acebb68cf"], "layout": "IPY_MODEL_c10d94e3e5334e4f99d0cf818664a5ac"}}, "3b8857e04be94395aad9d9e18cc69ac9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c4ae1b07ca644559d99314f2b9ef62a", "style": "IPY_MODEL_c35917fef39b4f198f8aec56f594a15e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4c4ae1b07ca644559d99314f2b9ef62a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c35917fef39b4f198f8aec56f594a15e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "94d119a56f1e40538383824b40ccd52c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6d0e48d53f242d39a49d67b6e254c3e", "style": "IPY_MODEL_825a48f7b51e44a0bbf888f731d56fcb", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the predicted\n                class probabilities.\n            </span></div>"}}, "e6d0e48d53f242d39a49d67b6e254c3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "825a48f7b51e44a0bbf888f731d56fcb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "92f93e8f9d934ccbab1fd0997d416cd1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95484a7b61854c67adda3e7c2156f747", "style": "IPY_MODEL_512686fee0584fe2aecf7c7dc8fc52d3", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "95484a7b61854c67adda3e7c2156f747": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "512686fee0584fe2aecf7c7dc8fc52d3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "53366b10ce7b40a6ab26db8019e18757": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.05021640003091432], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "58b2dea8-3961-4c51-8619-aa8c0a7766af"}, {"fill": "tozeroy", "line": {"color": "#00008b", "shape": "linear"}, "name": "Train Dataset", "x": 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"3542b815cbfb4e18800c612acebb68cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_65a048d56d464f9bb2953884602036a7", "style": "IPY_MODEL_a3f0b0ffe2984f71ba19159a2bc7aa76", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "65a048d56d464f9bb2953884602036a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a3f0b0ffe2984f71ba19159a2bc7aa76": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c10d94e3e5334e4f99d0cf818664a5ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aeead085d8204a30ac9676cb5c27677d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8e566413f5a1411685397afe8fb6edc0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_39a10908cc8945b69975f6b0846c9dba", "IPY_MODEL_6ad3da76b7e9468392a0da381c6925ba", "IPY_MODEL_7d9e0dcc46c84b11af852827ea99fb39", "IPY_MODEL_0be8dd3966fe40099ae80c572c661407"], "layout": "IPY_MODEL_5ffdaf49621a4605a3ed45419681b314"}}, "39a10908cc8945b69975f6b0846c9dba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f094706ec9d41fcbf71f83cbdd4bec1", "style": "IPY_MODEL_37691ab2b5d9434386ef0c38b342fed3", "value": "<h4 id=\"SimpleModelComparison_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>Simple Model Comparison</b></h4>"}}, "4f094706ec9d41fcbf71f83cbdd4bec1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37691ab2b5d9434386ef0c38b342fed3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6ad3da76b7e9468392a0da381c6925ba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3ccaa832a74441cd9d302e8ba511af3d", "style": "IPY_MODEL_68da179833814a598245da32776ae8ff", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3ccaa832a74441cd9d302e8ba511af3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "68da179833814a598245da32776ae8ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d9e0dcc46c84b11af852827ea99fb39": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcaa6e05a55f468fbb20e1dabb8fc95a", "style": "IPY_MODEL_b5f2c35446ad420fa589514f59ed9095", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_91ab9_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_91ab9_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_91ab9_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_91ab9_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_91ab9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_91ab9_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_91ab9_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_91ab9_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_91ab9_row0_col2\" class=\"data row0 col2\" >All classes passed, average gain for metrics: {'F1': '82.69%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "dcaa6e05a55f468fbb20e1dabb8fc95a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5f2c35446ad420fa589514f59ed9095": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0be8dd3966fe40099ae80c572c661407": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6f1925e45e3b462bbfbf701125fabb1b", "IPY_MODEL_528d70b60d6644d0a1c94a25424a72a4", "IPY_MODEL_80b88f321f394eb5b54f865848be1f25"], "layout": "IPY_MODEL_b359536672b543c4baaa959b72d2e9a3"}}, "6f1925e45e3b462bbfbf701125fabb1b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c295040d76f243b6800766c8ffca2a1d", "style": "IPY_MODEL_efd9659de157469fbb78369c57eb5f67", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c295040d76f243b6800766c8ffca2a1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "efd9659de157469fbb78369c57eb5f67": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "528d70b60d6644d0a1c94a25424a72a4": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=GradientBoostingClassifier model<br>Metric=F1<br>value=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "GradientBoostingClassifier model", "marker": {"color": "#636efa", "pattern": {"shape": ""}}, "name": "GradientBoostingClassifier model", "offsetgroup": "GradientBoostingClassifier model", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "05b9909d-bb5d-4bfa-82d5-93ed6d56c79d"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple model - most_frequent<br>Metric=F1<br>value=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple model - most_frequent", "marker": {"color": "#EF553B", "pattern": {"shape": ""}}, "name": "Simple model - most_frequent", "offsetgroup": "Simple model - most_frequent", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "6e566684-682f-4cfa-a9a5-f3b9ead62662"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "tickangle": 60, "tickprefix": "Class ", "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "mfDlkYzi7z9t/gqzGkXsPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "ugWKsbZ/7z8AAAAAAAAAAA=="}]}, "80b88f321f394eb5b54f865848be1f25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a069fb24d24642f8b3641e7483e8feaa", "style": "IPY_MODEL_e122f6c50c274c1bac3475732a27e3fa", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "a069fb24d24642f8b3641e7483e8feaa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e122f6c50c274c1bac3475732a27e3fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b359536672b543c4baaa959b72d2e9a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ffdaf49621a4605a3ed45419681b314": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "587aee3beb4748b1a8e75e86eeefa734": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_079d57179ce84252933c0489531e691a", "IPY_MODEL_a45b0397b9d2443a8e3fab8300f3485d", "IPY_MODEL_ee551f35f6da4041b5fb96b912a97048", "IPY_MODEL_480286bd8f57457291f2f66a3810f278"], "layout": "IPY_MODEL_4c6320ecca6c4bf8a7f89142745f4147"}}, "079d57179ce84252933c0489531e691a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8970e636274c4cd7915424dd4026c703", "style": "IPY_MODEL_918aabfb851a47909e877c130790519b", "value": "<h4 id=\"WeakSegmentsPerformance-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>Weak Segments Performance - Test Dataset</b></h4>"}}, "8970e636274c4cd7915424dd4026c703": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "918aabfb851a47909e877c130790519b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a45b0397b9d2443a8e3fab8300f3485d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_03a63034a98e4020b9899ef3230f5b57", "style": "IPY_MODEL_6d26a514c05546d29236fe1b1623ac30", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "03a63034a98e4020b9899ef3230f5b57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d26a514c05546d29236fe1b1623ac30": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee551f35f6da4041b5fb96b912a97048": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1ade7498596743759a5bf64a2e5a9218", "style": "IPY_MODEL_73cdd0b7158e42d0842a6b497d82203f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e65c6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e65c6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e65c6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e65c6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e65c6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e65c6_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e65c6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_e65c6_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_e65c6_row0_col2\" class=\"data row0 col2\" >Found a segment with accuracy score of 0.957 in comparison to an average score of 0.993 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1ade7498596743759a5bf64a2e5a9218": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73cdd0b7158e42d0842a6b497d82203f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "480286bd8f57457291f2f66a3810f278": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4db7c3bf8bb54a1ab9ead6e8f3b738e7", "IPY_MODEL_8b739c5378ec4ae0882aa2947bf1020a", "IPY_MODEL_5e208a456fdf483c964f87b622c4695d", "IPY_MODEL_7149d74fdfe9464bbf70657a7fbd1f39"], "layout": "IPY_MODEL_58d65e9070754ae79693d0af9ca44dd6"}}, "4db7c3bf8bb54a1ab9ead6e8f3b738e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c8bcbc426f3413da79c41ee61dbbfcf", "style": "IPY_MODEL_d8fcb1b1c5d945319acae937d21d2e1a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "2c8bcbc426f3413da79c41ee61dbbfcf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8fcb1b1c5d945319acae937d21d2e1a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8b739c5378ec4ae0882aa2947bf1020a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9cc9aa9ecedb4a118bff671562a3f00d", "style": "IPY_MODEL_4b67a077a9b0485088ffa3a70f1744a1", "value": "<div>Showcasing intersections of features with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "9cc9aa9ecedb4a118bff671562a3f00d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b67a077a9b0485088ffa3a70f1744a1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5e208a456fdf483c964f87b622c4695d": {"model_name": "VBoxModel", "model_module": 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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Accuracy Score (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "dsr"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "636f6cf61a1245ff95396462fd26a65f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7e27ed056fba423abf88823adc4c26c1", "style": "IPY_MODEL_bc7d0aea107f49ef8131ac34adf79391", "value": "<div>Check ran on 2724 data samples. Accuracy Score on the full data set is 0.99.</div>"}}, "7e27ed056fba423abf88823adc4c26c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc7d0aea107f49ef8131ac34adf79391": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d7f9618687024978af1c9b7c2a7ceee5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "89b4e90fd1dd4b3ba6f6f9173cc0a84a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "74d0439b729540c49bbf233d08d28062": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7149d74fdfe9464bbf70657a7fbd1f39": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a43d096f76af464fac32c9c758caadb3", "style": "IPY_MODEL_e0484b8f13af40428f645df2c045ed86", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "a43d096f76af464fac32c9c758caadb3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e0484b8f13af40428f645df2c045ed86": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58d65e9070754ae79693d0af9ca44dd6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c6320ecca6c4bf8a7f89142745f4147": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6841203e928d45bfb4f2b84d05861b86": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_d97366a2b81546668b15e62f438a5125", "IPY_MODEL_ec9c251cc1fc4dad877a91842969ed11", "IPY_MODEL_f2fd056a151142d7aab620b5205f9281", "IPY_MODEL_f213b0e947e147bb84e277aa83c1624f"], "layout": "IPY_MODEL_cb54232081c54bed95f92b3823a03003"}}, "d97366a2b81546668b15e62f438a5125": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0b121a1c0621448c96373845d6b84543", "style": "IPY_MODEL_a8bee453c59d4ff49acced5b4a426b01", "value": "<h4 id=\"BoostingOverfit_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>Boosting Overfit</b></h4>"}}, "0b121a1c0621448c96373845d6b84543": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8bee453c59d4ff49acced5b4a426b01": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec9c251cc1fc4dad877a91842969ed11": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f161fb0f76c4d51bbed8c7ca3a153f1", "style": "IPY_MODEL_2fae553aaf394693b3a0ed2f3396dca8", "value": "<p>Check for overfit caused by using too many iterations in a gradient boosted model. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_boosting_overfit.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5f161fb0f76c4d51bbed8c7ca3a153f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2fae553aaf394693b3a0ed2f3396dca8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f2fd056a151142d7aab620b5205f9281": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d78235e12584428da573535391face1a", "style": "IPY_MODEL_b13f8e464e7f47d0b6b932c28ad16bc3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c23b5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c23b5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c23b5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c23b5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c23b5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c23b5_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c23b5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c23b5_row0_col1\" class=\"data row0 col1\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_c23b5_row0_col2\" class=\"data row0 col2\" >Found score decline of -0.11%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d78235e12584428da573535391face1a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b13f8e464e7f47d0b6b932c28ad16bc3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f213b0e947e147bb84e277aa83c1624f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c3c62781e12044d6abe3551cf709ee96", "IPY_MODEL_3a966f9d3e8a4e7db60b33ef3158c065", "IPY_MODEL_2ebd5ea087c34454a618dc411dc355dc", "IPY_MODEL_f57560fd486846ad8a349c95075719ad"], "layout": "IPY_MODEL_674c2e1fdd7d496d9f85648b3a099038"}}, "c3c62781e12044d6abe3551cf709ee96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_feb08825606e447e8464abbc4ff3e2dc", "style": "IPY_MODEL_cc01f5afa7064ecab8ba1d684d370f20", "value": "<h5><b>Additional Outputs</b></h5>"}}, "feb08825606e447e8464abbc4ff3e2dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc01f5afa7064ecab8ba1d684d370f20": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a966f9d3e8a4e7db60b33ef3158c065": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a05c7ea9361c44628f496c0ad0274259", "style": "IPY_MODEL_6e771c5d36784969b9f0e526f57fe274", "value": "<div><span>\n                The check limits the boosting model to using up to N estimators each time, and plotting the\n                Accuracy calculated for each subset of estimators for both the train dataset and the test dataset.\n            </span></div>"}}, "a05c7ea9361c44628f496c0ad0274259": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e771c5d36784969b9f0e526f57fe274": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2ebd5ea087c34454a618dc411dc355dc": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"mode": "lines+markers", "name": "Training score", "x": [1, 2, 3, 4, 5, 22, 38, 54, 71, 87, 103, 120, 136, 152, 169, 185, 201, 218, 234, 250], "y": {"dtype": "float64", "shape": [20]}, "type": "scatter", "uid": "1f232f12-b21c-496d-88f1-45c336f87d39"}, {"mode": "lines+markers", "name": "Test score", "x": [1, 2, 3, 4, 5, 22, 38, 54, 71, 87, 103, 120, 136, 152, 169, 185, 201, 218, 234, 250], "y": {"dtype": "float64", "shape": [20]}, "type": "scatter", "uid": "152ef2c6-4186-44d2-aebb-b58ac4eda2ec"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 500, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", 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"white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Accuracy score compared to number of boosting iteration"}, "xaxis": {"title": {"text": "Number of boosting iterations"}}, "yaxis": {"title": {"text": "Accuracy"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "dFNSHWID7z90U1IdYgPvP3RTUh1iA+8/dFNSHWID7z90U1IdYgPvP8Jupuy13+8/3mCgwhn+7z8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "dFNSHWID7z90U1IdYgPvP3RTUh1iA+8/dFNSHWID7z90U1IdYgPvP/1gzuPSt+8/01CBaNrD7z/TUIFo2sPvP9NQgWjaw+8/s4QHDODM7z+oQDTt4c/vP9NQgWjaw+8/yAyuSdzG7z/IDK5J3MbvP8gMrkncxu8/yAyuSdzG7z/IDK5J3MbvP8gMrkncxu8/yAyuSdzG7z/IDK5J3MbvPw=="}]}, "f57560fd486846ad8a349c95075719ad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e2bef57d0f1c436280df9852feb99ff8", "style": "IPY_MODEL_84a1a233c95c4892873b056aa74f7954", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "e2bef57d0f1c436280df9852feb99ff8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "84a1a233c95c4892873b056aa74f7954": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "674c2e1fdd7d496d9f85648b3a099038": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cb54232081c54bed95f92b3823a03003": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c913baa21b884d5186320c31f653fc4c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_3edf5d8cd78a44508494843418826be6", "IPY_MODEL_da743d028a5c412ca0725f0caaafd813", "IPY_MODEL_60929d9900834034a11d7fe3860c2dfa", "IPY_MODEL_46a1c57a99d24d6d809c020d289d47f4"], "layout": "IPY_MODEL_7210737cfc924f42a31e24c3f6977025"}}, "3edf5d8cd78a44508494843418826be6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db2785b997e74ab5a29583681f38f589", "style": "IPY_MODEL_ebe623c7669c431da7b7e3a377d5d67b", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "db2785b997e74ab5a29583681f38f589": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ebe623c7669c431da7b7e3a377d5d67b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da743d028a5c412ca0725f0caaafd813": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10d5cbc5bc8a40058af6137810d7a3e5", "style": "IPY_MODEL_8cb6f77a3a3948b68c80164150de8e3e", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "10d5cbc5bc8a40058af6137810d7a3e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8cb6f77a3a3948b68c80164150de8e3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "60929d9900834034a11d7fe3860c2dfa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db56a3a190084fe18dd9f7cee189328d", "style": "IPY_MODEL_35fc2e7e75c5480a9b9eba7596e27275", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_0187e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0187e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0187e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0187e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0187e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0187e_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0187e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_0187e_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_0187e_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.878e-05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "db56a3a190084fe18dd9f7cee189328d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "35fc2e7e75c5480a9b9eba7596e27275": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "46a1c57a99d24d6d809c020d289d47f4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ef5bc8a0e42e4647ab6c3c50117f3251", "IPY_MODEL_6ef68f5ccd3f46df8fbb5cb3a3a0cc85", "IPY_MODEL_f135269ac09547ccba85df9e5f3d79cf", "IPY_MODEL_4a8878d133e440f59f76af727c804e73"], "layout": "IPY_MODEL_7247a76826d048a89ddc85638b517efa"}}, "ef5bc8a0e42e4647ab6c3c50117f3251": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ba29b3d22aa04b759715d8cef793819a", "style": "IPY_MODEL_69cebea78f744ef68b3182c098b65925", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ba29b3d22aa04b759715d8cef793819a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69cebea78f744ef68b3182c098b65925": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6ef68f5ccd3f46df8fbb5cb3a3a0cc85": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c4d5c1c82954b91abb356ddce959088", "style": "IPY_MODEL_85d952b2820641b5a5f0d0ed46bd31b2", "value": "<div>Average model inference time for one sample (in seconds): 1.878e-05</div>"}}, "4c4d5c1c82954b91abb356ddce959088": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "85d952b2820641b5a5f0d0ed46bd31b2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f135269ac09547ccba85df9e5f3d79cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3cf2b690d7884074b9ae84978af0e769", "style": "IPY_MODEL_3c9bc3f004e741e8aeb36ad63ffa50e4", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 8626. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "3cf2b690d7884074b9ae84978af0e769": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c9bc3f004e741e8aeb36ad63ffa50e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4a8878d133e440f59f76af727c804e73": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_71b15160aa86425f8fb8ebd940c12086", "style": "IPY_MODEL_17bdcfbd768647f1a6241f849c765b9f", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "71b15160aa86425f8fb8ebd940c12086": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "17bdcfbd768647f1a6241f849c765b9f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7247a76826d048a89ddc85638b517efa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7210737cfc924f42a31e24c3f6977025": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8e9c1d2cd09e4bd7be7d4663c243fefb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2f62da501016476da64982c1f1c48311", "IPY_MODEL_e0f4ab5adedc439f8d166a81dba9e9c5", "IPY_MODEL_d13e05e4027c484d9614672dfcfc3066", "IPY_MODEL_ef4bab8f1a70406aabecdb57646d7e1c"], "layout": "IPY_MODEL_07f4ff3591054b5da053356936f2478a"}}, "2f62da501016476da64982c1f1c48311": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_849158e010a94cbb9f9ec9b538555888", "style": "IPY_MODEL_3bd9d376899b4738b806cfc8989fbc19", "value": "<h4 id=\"ModelInferenceTime-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "849158e010a94cbb9f9ec9b538555888": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bd9d376899b4738b806cfc8989fbc19": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0f4ab5adedc439f8d166a81dba9e9c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ad9ad5efd6af4d20b55f84d2da7df550", "style": "IPY_MODEL_1f74f1929bdb409789a0c5b96783aece", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ad9ad5efd6af4d20b55f84d2da7df550": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f74f1929bdb409789a0c5b96783aece": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d13e05e4027c484d9614672dfcfc3066": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67f0d6d395da46ff8967e3af993a421e", "style": "IPY_MODEL_dd804d6177474a2fb29e772edc0f48da", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_88252_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_88252_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_88252_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_88252_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_88252_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_88252_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Status</th>\n      <th class=\"col_heading level0 col1\" >Condition</th>\n      <th class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_88252_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_88252_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_88252_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.899e-05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "67f0d6d395da46ff8967e3af993a421e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd804d6177474a2fb29e772edc0f48da": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef4bab8f1a70406aabecdb57646d7e1c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b82fb320d61e4d75b9609842a69e2303", "IPY_MODEL_2ccaae582f5b428983009a920204c27b", "IPY_MODEL_b7b83c491c6b4a95bb687b2acccc45bd", "IPY_MODEL_e9a7638878604393be24c73073fe660a"], "layout": "IPY_MODEL_e337b36ed9954fdf859270d27ce55b20"}}, "b82fb320d61e4d75b9609842a69e2303": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_45dd5f098a214e818cbcda7aa9c82471", "style": "IPY_MODEL_5e002430a48d4f489ac7adc07d7ae71d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "45dd5f098a214e818cbcda7aa9c82471": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e002430a48d4f489ac7adc07d7ae71d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2ccaae582f5b428983009a920204c27b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bda38b5e954244299425fe279de2af85", "style": "IPY_MODEL_f468ba8a15dc468d913ae2887e1e25f1", "value": "<div>Average model inference time for one sample (in seconds): 1.899e-05</div>"}}, "bda38b5e954244299425fe279de2af85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f468ba8a15dc468d913ae2887e1e25f1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7b83c491c6b4a95bb687b2acccc45bd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b798dd95ce2439f866ae1eb303fb9bc", "style": "IPY_MODEL_ac26c8cb78764bb1a3afcc1c17d45a2d", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 2724. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "6b798dd95ce2439f866ae1eb303fb9bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ac26c8cb78764bb1a3afcc1c17d45a2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9a7638878604393be24c73073fe660a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_60b1ff0dc92b45478aa699edddd01d25", "style": "IPY_MODEL_856db84fd43048158f5fa4cd3c29ecf8", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-Z1LMJ\" style=\"font-size: 14px;\">Go to top</a>"}}, "60b1ff0dc92b45478aa699edddd01d25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "856db84fd43048158f5fa4cd3c29ecf8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e337b36ed9954fdf859270d27ce55b20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07f4ff3591054b5da053356936f2478a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd37056f06a9472ea243fad636bd4281": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aded99053d1949b5b25f92f594b911ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ce9f28caff494153a18bf36c79d2f3ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "87c7dba84bb845d2bde29889075803ce": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ebcd8618621d4f989f64c31e60f49a6a", "IPY_MODEL_df2d9333b72640899cf980b4939d4b4a"], "layout": "IPY_MODEL_8e26a99bbd8e40858a30ff506098af08"}}, "ebcd8618621d4f989f64c31e60f49a6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_497e81a9650148fab7ae67caac5013e0", "style": "IPY_MODEL_618749bcb31e4520b0b35a6bbef714ee", "value": "<span id=\"summary_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\"></span>"}}, "497e81a9650148fab7ae67caac5013e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "618749bcb31e4520b0b35a6bbef714ee": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "df2d9333b72640899cf980b4939d4b4a": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_9ae7c095d5a74bfa96a4a5c867ee2f26"], "layout": "IPY_MODEL_32e2fd3de9e84c97b7eed1fe1e78e40b", "selected_index": null}}, "9ae7c095d5a74bfa96a4a5c867ee2f26": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d9a0e2ba3c5941d692dfef40c375c5cf", "IPY_MODEL_e75f8d245ab3427699b1181a9eef2f36", "IPY_MODEL_386589372a934d25b6b8bddaa0f7822d", "IPY_MODEL_3a524aa16f064eb58d02eca38b585125", "IPY_MODEL_ddd50d57adf64aa0a31f52d6fb2e90e6", "IPY_MODEL_3a524aa16f064eb58d02eca38b585125", "IPY_MODEL_8b7ad85f40ce4d1b883e73e836cd231e", "IPY_MODEL_3a524aa16f064eb58d02eca38b585125", "IPY_MODEL_ae82ba7e4fab4928b672d5f61e93c59a"], "layout": "IPY_MODEL_7f2d3154287a4bca9bd0ba7b4765c3c1"}}, "d9a0e2ba3c5941d692dfef40c375c5cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6206567e325546628ec32738b1552e8f", "style": "IPY_MODEL_ef94cf64d7fd4d3b84923abb7d666b9d", "value": "<style type=\"text/css\">\n#T_6f136_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f136_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f136_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f136_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f136_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6f136_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6f136_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrixReport-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\">Confusion Matrix Report - Train Dataset</a></td>\n      <td id=\"T_6f136_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_6f136_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrixReport-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\">Confusion Matrix Report - Test Dataset</a></td>\n      <td id=\"T_6f136_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_6f136_row2_col0\" class=\"data row2 col0\" ><a href=\"#CalibrationMetric-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\">Calibration Metric - Train Dataset</a></td>\n      <td id=\"T_6f136_row2_col1\" class=\"data row2 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_6f136_row3_col0\" class=\"data row3 col0\" ><a href=\"#CalibrationMetric-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\">Calibration Metric - Test Dataset</a></td>\n      <td id=\"T_6f136_row3_col1\" class=\"data row3 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6206567e325546628ec32738b1552e8f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef94cf64d7fd4d3b84923abb7d666b9d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e75f8d245ab3427699b1181a9eef2f36": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0e133e57e57042ce8d65cb84f27d2c84", "style": "IPY_MODEL_0e9a808e28d34302b8c98b37fa20b8c2", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "0e133e57e57042ce8d65cb84f27d2c84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e9a808e28d34302b8c98b37fa20b8c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "386589372a934d25b6b8bddaa0f7822d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_9a5b6149a6024a1bb3d4d9f90986136d", "IPY_MODEL_0f21853ae6fb484f8e2c6eff02e2a06a", "IPY_MODEL_6fa2fdeea23e4620aed0f5436a80824f"], "layout": "IPY_MODEL_61ebfe8d3f1948c5b9cdbdb229571b51"}}, "9a5b6149a6024a1bb3d4d9f90986136d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_231c6b7b1ff94828be1b8b6fe4cff5f5", "style": "IPY_MODEL_4b5623dcc758408ab4357fb8a3955c71", "value": "<h4 id=\"ConfusionMatrixReport-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\"><b>Confusion Matrix Report - Train Dataset</b></h4>"}}, "231c6b7b1ff94828be1b8b6fe4cff5f5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b5623dcc758408ab4357fb8a3955c71": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f21853ae6fb484f8e2c6eff02e2a06a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_04c980676a724c8ab5c5c01e34ac7965", "style": "IPY_MODEL_71d20291577c4f7487d004324ea4e666", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "04c980676a724c8ab5c5c01e34ac7965": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71d20291577c4f7487d004324ea4e666": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6fa2fdeea23e4620aed0f5436a80824f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_34d77d40f38e4740a83808dfb88af6e4", "IPY_MODEL_ddc21dbaa795441d8b2920498b781541", "IPY_MODEL_f91b48dd25754475bafec5e186a80e0f", "IPY_MODEL_62ad3197e74445df9acf55bc87801f75"], "layout": "IPY_MODEL_68464df134a341618db4b58b6b626fc9"}}, "34d77d40f38e4740a83808dfb88af6e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cb77842f92ff4ba0a593d41397d76bbe", "style": "IPY_MODEL_acaef8b61f8345e7987c745e735e2cc8", "value": "<h5><b>Additional Outputs</b></h5>"}}, "cb77842f92ff4ba0a593d41397d76bbe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "acaef8b61f8345e7987c745e735e2cc8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ddc21dbaa795441d8b2920498b781541": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_602f4e86dc08491a999dc3a4cde27710", "style": "IPY_MODEL_42529f70910b486db8454d064dbc1616", "value": "<div>The overall accuracy of your model is: 100.0%.</div>"}}, "602f4e86dc08491a999dc3a4cde27710": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42529f70910b486db8454d064dbc1616": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f91b48dd25754475bafec5e186a80e0f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[96.92, 100.0, 100.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [3.08, 100.0, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[8360, 0], [0, 266]], "texttemplate": "%{text}", "x": ["0", "1"], "xgap": 1, "y": ["0", "1"], "ygap": 1, "z": [["100", "0"], ["0", "100"]], "type": "heatmap", "uid": "1aee843d-2abc-4407-ba57-3fe9fafabced"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "62ad3197e74445df9acf55bc87801f75": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_834b6c960bbf49a3b58e02c9a74dcf93", "style": "IPY_MODEL_208976a1cd0e49ad99f86153ad88e252", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "834b6c960bbf49a3b58e02c9a74dcf93": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "208976a1cd0e49ad99f86153ad88e252": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "68464df134a341618db4b58b6b626fc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61ebfe8d3f1948c5b9cdbdb229571b51": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a524aa16f064eb58d02eca38b585125": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2270b73d159f4f409e40e90620575d21", "style": "IPY_MODEL_9864ae98dc694277a916c7c358765eef", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "2270b73d159f4f409e40e90620575d21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9864ae98dc694277a916c7c358765eef": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ddd50d57adf64aa0a31f52d6fb2e90e6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_dff784a96e25498f82486ab362188a44", "IPY_MODEL_bbc905536a894eee87866a0500155ddc", "IPY_MODEL_33c442f3feb249698a880c1db548cb4a"], "layout": "IPY_MODEL_e1c77ad586fb46a6990f1163e123449f"}}, "dff784a96e25498f82486ab362188a44": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dae2f464925b499ea549c9ac0e89e900", "style": "IPY_MODEL_12945ee4160b4a9d80658555fba3d324", "value": "<h4 id=\"ConfusionMatrixReport-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\"><b>Confusion Matrix Report - Test Dataset</b></h4>"}}, "dae2f464925b499ea549c9ac0e89e900": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "12945ee4160b4a9d80658555fba3d324": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bbc905536a894eee87866a0500155ddc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3299eb5ba2254cd3a819860e1bef83df", "style": "IPY_MODEL_03fd1dd7e9b54308b15362e23b019ca4", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3299eb5ba2254cd3a819860e1bef83df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03fd1dd7e9b54308b15362e23b019ca4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "33c442f3feb249698a880c1db548cb4a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c17db4ed4a00477296144c52a9f0c382", "IPY_MODEL_28817e556f374a4399913be2d0025837", "IPY_MODEL_6dc90e8955a14259a845fedd6e1b399a", "IPY_MODEL_facf3d6e26764be4a4df12aff46d5083"], "layout": "IPY_MODEL_b9ed7ddc1ba94c03bdaa07d758104a48"}}, "c17db4ed4a00477296144c52a9f0c382": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1ae703c4db2647c38262302e3a86138c", "style": "IPY_MODEL_0e43008f5d9242428c84acd8df3e1ced", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1ae703c4db2647c38262302e3a86138c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e43008f5d9242428c84acd8df3e1ced": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "28817e556f374a4399913be2d0025837": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8c80c145dd6d4ecaba73049d54101957", "style": "IPY_MODEL_e873b14d124f41499169fa73fcad904a", "value": "<div>The overall accuracy of your model is: 92.72%.<br>Best accuracy achieved on samples with <b>0</b> label (99.73%).<br>Worst accuracy achieved on samples with <b>1</b> label (85.71%).</div>"}}, "8c80c145dd6d4ecaba73049d54101957": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e873b14d124f41499169fa73fcad904a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6dc90e8955a14259a845fedd6e1b399a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[96.66, 99.73, 99.55], [0.26, 0.27, 0.26]], [[0.44, 14.29, 15.19], [2.64, 85.71, 91.14]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[2633, 7], [12, 72]], "texttemplate": "%{text}", "x": ["0", "1"], "xgap": 1, "y": ["0", "1"], "ygap": 1, "z": [["99.73", "0.27"], ["14.29", "85.71"]], "type": "heatmap", "uid": "d9e73c3f-6d82-4f2b-95a3-0aab12f72bd3"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": 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"_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "facf3d6e26764be4a4df12aff46d5083": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fbf355943c0c4edf9638b773787692f4", "style": "IPY_MODEL_35139f3fa7e84447ab0308bda3ba0c80", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "fbf355943c0c4edf9638b773787692f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "35139f3fa7e84447ab0308bda3ba0c80": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b9ed7ddc1ba94c03bdaa07d758104a48": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e1c77ad586fb46a6990f1163e123449f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b7ad85f40ce4d1b883e73e836cd231e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_8a773777cb6448c08c0f85a6dc49ce67", "IPY_MODEL_46bc199f9212426a8b20852e52897be8", "IPY_MODEL_93e49209dd8b41a297f53746f54d1e2d"], "layout": "IPY_MODEL_6cae10303c734172a92499eebc73729f"}}, "8a773777cb6448c08c0f85a6dc49ce67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_88cafbd477214cbabc9c8c5ad30346d2", "style": "IPY_MODEL_a1ba511bb0944235bc39c2c812d095a0", "value": "<h4 id=\"CalibrationMetric-TrainDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\"><b>Calibration Metric - Train Dataset</b></h4>"}}, "88cafbd477214cbabc9c8c5ad30346d2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1ba511bb0944235bc39c2c812d095a0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "46bc199f9212426a8b20852e52897be8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb4dd376ba3e4956bf580ba251df5421", "style": "IPY_MODEL_54339a4b456346b79330ef386e073a3f", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "fb4dd376ba3e4956bf580ba251df5421": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54339a4b456346b79330ef386e073a3f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93e49209dd8b41a297f53746f54d1e2d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cc9f040712164affba2cac88c47556f2", "IPY_MODEL_100c0d72696c4f19b844b7aee20a4506", "IPY_MODEL_7163a227e8cd49daa4093ff936cf3f44", "IPY_MODEL_dec2de5254ab410da251551ea3618db0", "IPY_MODEL_4908c7602a4341cc9d38a5fcbcaad28f"], "layout": "IPY_MODEL_3a0ebba697b0460ebee29559c6574360"}}, "cc9f040712164affba2cac88c47556f2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_66613be54e19485eb16e6f4e4d189ef4", "style": "IPY_MODEL_eee94dbb127742d9a8ad42aa97ee7137", "value": "<h5><b>Additional Outputs</b></h5>"}}, "66613be54e19485eb16e6f4e4d189ef4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eee94dbb127742d9a8ad42aa97ee7137": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "100c0d72696c4f19b844b7aee20a4506": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d10479bb656746c6883cf6e9fbc97080", "style": "IPY_MODEL_72b2bd9dfaf04f68a88aaec4035f69af", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.</div>"}}, "d10479bb656746c6883cf6e9fbc97080": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72b2bd9dfaf04f68a88aaec4035f69af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7163a227e8cd49daa4093ff936cf3f44": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "bad8888e-5072-4a6f-810e-4c52e78cc0fc"}, {"mode": "lines+markers", "name": "(brier:   0.0000)", "x": {"dtype": "float64", "shape": [2]}, "y": {"dtype": "float64", "shape": [2]}, "type": "scatter", "uid": 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"zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Calibration plots (reliability curve)"}, "xaxis": {"title": {"text": "Mean predicted value"}}, "yaxis": {"title": {"text": "Fraction of positives"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "9vMh1lUyRjwR+v/////vPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}]}, "dec2de5254ab410da251551ea3618db0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_caf333dc9ebc4777978ee5551f1c60c1", "style": "IPY_MODEL_bcb271b3d10b446f8b1239ac6a522379", "value": "<div>The Brier score metric may be used to assess how well a classifier is calibrated. For more info, please visit https://en.wikipedia.org/wiki/Brier_score</div>"}}, "caf333dc9ebc4777978ee5551f1c60c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bcb271b3d10b446f8b1239ac6a522379": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4908c7602a4341cc9d38a5fcbcaad28f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_971548655a4b40199f7bbd6f7970dfb4", "style": "IPY_MODEL_64605aa9add64798b3894557c8ff08b0", "value": "<br><a href=\"#summary_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "971548655a4b40199f7bbd6f7970dfb4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64605aa9add64798b3894557c8ff08b0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a0ebba697b0460ebee29559c6574360": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6cae10303c734172a92499eebc73729f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae82ba7e4fab4928b672d5f61e93c59a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_3238e53389ad4e68b3355f66ce5f97c0", "IPY_MODEL_deb98b0b206a4ff4bd4b0205a2eb5311", "IPY_MODEL_aecdd158c27f48d7902388056fbde660"], "layout": "IPY_MODEL_eb121e8325054056b5a698df50443a2f"}}, "3238e53389ad4e68b3355f66ce5f97c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a01d3f47009e4cce93425b778d8f0745", "style": "IPY_MODEL_54dbb36f117541bd9522bdfcbb531cc0", "value": "<h4 id=\"CalibrationMetric-TestDataset_N07ILCGSO1HSYU4P23PBR9L0X-section-9UY5G\"><b>Calibration Metric - Test Dataset</b></h4>"}}, "a01d3f47009e4cce93425b778d8f0745": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54dbb36f117541bd9522bdfcbb531cc0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "deb98b0b206a4ff4bd4b0205a2eb5311": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_368e41752d3049dd9530d09c342cfc2b", "style": "IPY_MODEL_80df1d4280b04ff4bbb23495a24c5184", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "368e41752d3049dd9530d09c342cfc2b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80df1d4280b04ff4bbb23495a24c5184": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aecdd158c27f48d7902388056fbde660": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_79ff2bf6515c4c2fafa74916139b0916", "IPY_MODEL_2cc4d19c9b4b446e992f6c2ce4eaf3b4", "IPY_MODEL_a2345e2298c34534a5625d1b7a36f3e4", "IPY_MODEL_5eaf8c160b254d7aa2999fbeb6e4e657", "IPY_MODEL_89e38bd82b2f4deeaed5b036a069afce"], "layout": "IPY_MODEL_afbe2ad3b1d247a384e32fcd4df807f9"}}, "79ff2bf6515c4c2fafa74916139b0916": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a45223b6a1c482d9c8d6e11d8d5b6df", "style": "IPY_MODEL_45893457e1db439c904004ac43856aba", "value": "<h5><b>Additional Outputs</b></h5>"}}, "0a45223b6a1c482d9c8d6e11d8d5b6df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45893457e1db439c904004ac43856aba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2cc4d19c9b4b446e992f6c2ce4eaf3b4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b2d19f1aa9c43c1be019bc5b6c091fa", "style": "IPY_MODEL_37e4a0eb39024e71a9fae68109197867", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.</div>"}}, "1b2d19f1aa9c43c1be019bc5b6c091fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37e4a0eb39024e71a9fae68109197867": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a2345e2298c34534a5625d1b7a36f3e4": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "172d2bf7-ed78-43ad-89e0-c562ed807338"}, {"mode": "lines+markers", "name": "(brier:   0.0070)", "x": {"dtype": "float64", "shape": [4]}, "y": {"dtype": "float64", "shape": [4]}, "type": "scatter", "uid": "effdc03b-900c-40fe-b609-ffcf1682cddb"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 500, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, 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.. GENERATED FROM PYTHON SOURCE LINES 384-416

Understanding the checks' results!
==================================
Again, ``deepchecks`` supplied some interesting insights, including a considerable
performance degradation between the train and test sets. We can see that the
degradation in performance between the train and test set that we witnessed before
was mitigated only very little.

However, for a boosted model we get a pretty cool *Boosting Overfit* check that
plots the accuracy of the model along increasing boosting iterations of the model.
This can help us see that we might have a minor case of overfitting here, as train
set accuracy is achieved rather early on, and while test set performance improve
for a little while longer, they show some degradation starting from iteration 135.

This at least points to possible value in adjusting the ``n_estimators``
parameter, either reducing it or increasing it to see if degradation continues or
perhaps the trends shifts.

Wrapping it all up!
===================
We haven't got a decent model yet, but ``deepchecks`` provides us with numerous
tools to help us navigate our development and make better feature engineering
and model selection decisions, by easily making critical issues in data drift,
overfitting, leakage, feature importance and model calibration readily accessible.

And this is just what ``deepchecks`` can do out of the box, with the prebuilt
checks and suites! There is a lot more potential in the way the package lends
itself to easy customization and creation of checks and suites tailored to your
needs. We will touch upon some such advanced uses in future guides.

We, however, hope this example can already provide you with a good starting point
for getting some immediate benefit out of using deepchecks! Have fun, and reach
out to us if you need assistance! :)


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (3 minutes 54.179 seconds)


.. _sphx_glr_download_tabular_auto_tutorials_other_plot_phishing_urls.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_phishing_urls.py <plot_phishing_urls.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_phishing_urls.ipynb <plot_phishing_urls.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_