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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_user-guide_tabular_auto_tutorials_plot_phishing_urls.py>`
        to download the full example code

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

.. _sphx_glr_user-guide_tabular_auto_tutorials_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:
    |#########   | 9/12 [Time: 00:04, Check=Outlier Sample Detection]
    Data Integrity Suite:
    |########### | 11/12 [Time: 00:05, Check=Feature Feature Correlation]
                                                                                           

.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <!--
        ~ ----------------------------------------------------------------------------
        ~ Copyright (C) 2021-2022 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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    {"version_major": 2, "version_minor": 0, "state": {"fa231fc9c2c84bf78fdfb562e3605a83": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Data Integrity Suite"}, "children": ["IPY_MODEL_14d9af71625a4f3b9374a02a08f3b52d"], "layout": "IPY_MODEL_2f550f1541d34fb1a220915e759375bd"}}, "14d9af71625a4f3b9374a02a08f3b52d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_994bf3dec12843c0bb09f007991fe95e", "IPY_MODEL_15ba3a521a6a4fc5a755aad7796460ce", "IPY_MODEL_e7ec5295231c423694cf22f7ddd90d11", "IPY_MODEL_14025001d683470096230fdd4307f25e", "IPY_MODEL_9c8cfead37a244578835b93d92d252db"], "layout": "IPY_MODEL_455cdd55cd5f411dbd2d5c1cd1144d1b"}}, "994bf3dec12843c0bb09f007991fe95e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6ef22412074547ff940fb20c378b82aa", "style": "IPY_MODEL_78c48d3359bc4620813d92fdbb3d9eae", "value": "\n            <h1 id=\"summary_LPYS280XKXWD8IL3FB9W1Q5UZ\">Data Integrity Suite</h1>\n            <p>\n                The suite is composed of various checks such as: Feature Label Correlation, Mixed Data Types, Data Duplicates, etc...<br>\n                Each check may contain conditions (which will result in pass / fail / warning / error\n                , represented by \n<span style=\"color: green;display:inline-block\">\u2713</span> /\n<span style=\"color: red;display:inline-block\">\u2716</span> /\n<span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> /\n<span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>\n) as well as other outputs such as plots or tables.<br>\n                Suites, checks and conditions can all be modified. Read more about\n                <a href=https://docs.deepchecks.com/en/stable/examples/guides/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"}}, "6ef22412074547ff940fb20c378b82aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "78c48d3359bc4620813d92fdbb3d9eae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "15ba3a521a6a4fc5a755aad7796460ce": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1b054bf147c54e68ac7b3f16cbcbce68", "IPY_MODEL_ef2934b8bfcb4bb4b3ba5828461ad787"], "layout": "IPY_MODEL_2ed30b9b51bb41aa9b88499ff919750e"}}, "1b054bf147c54e68ac7b3f16cbcbce68": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_98ea073583f54960b3bbd89f08e73a6d", "style": "IPY_MODEL_1d3c95f8e6ed4559aa623992a9ae087e", "value": "<span id=\"summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\"></span>"}}, "98ea073583f54960b3bbd89f08e73a6d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1d3c95f8e6ed4559aa623992a9ae087e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef2934b8bfcb4bb4b3ba5828461ad787": {"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_044ec0a0a2d64fe28489e2ab94c69fcf"], "layout": "IPY_MODEL_df0c576ff36a43e2ac62ee4c7d77794e", "selected_index": null}}, "044ec0a0a2d64fe28489e2ab94c69fcf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_59a2112b8dba464dba735192b019c27f", "IPY_MODEL_cbfc255a28304e909d1613798e216a61", "IPY_MODEL_1e2a718f09084e9588daf833b1ae6265", "IPY_MODEL_82e31c4debdb4fd0b586a58725bbeeeb", "IPY_MODEL_0213ff2c340a48f5b222c6433471a07f", "IPY_MODEL_82e31c4debdb4fd0b586a58725bbeeeb", "IPY_MODEL_43422c4af42d4bbb94cddb2207382d77"], "layout": "IPY_MODEL_ccd526eed43d45bdb8660c57b90f0ee5"}}, "59a2112b8dba464dba735192b019c27f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11c8f8264d0c4cd8978a0828508a0dac", "style": "IPY_MODEL_eecb8cc56b5348d1af23b8dc569b32bf", "value": "<style type=\"text/css\">\n#T_db3a4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_db3a4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_db3a4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_db3a4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_db3a4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_db3a4_\">\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_db3a4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_db3a4_row0_col1\" class=\"data row0 col1\" ><a href=\"#SingleValueinColumn_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\">Single Value in Column</a></td>\n      <td id=\"T_db3a4_row0_col2\" class=\"data row0 col2\" >Does not contain only a single value</td>\n      <td id=\"T_db3a4_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_db3a4_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_db3a4_row1_col1\" class=\"data row1 col1\" ><a href=\"#Feature-FeatureCorrelation_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\">Feature-Feature Correlation</a></td>\n      <td id=\"T_db3a4_row1_col2\" class=\"data row1 col2\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_db3a4_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    <tr>\n      <td id=\"T_db3a4_row2_col0\" class=\"data row2 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_db3a4_row2_col1\" class=\"data row2 col1\" ><a href=\"#DataDuplicates_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\">Data Duplicates</a></td>\n      <td id=\"T_db3a4_row2_col2\" class=\"data row2 col2\" >Duplicate data ratio is less or equal to 0%</td>\n      <td id=\"T_db3a4_row2_col3\" class=\"data row2 col3\" >Found 0.0088% duplicate data</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "11c8f8264d0c4cd8978a0828508a0dac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eecb8cc56b5348d1af23b8dc569b32bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbfc255a28304e909d1613798e216a61": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5751f6cd979e4227846c62ebd8e1d8a4", "style": "IPY_MODEL_54ab2397ce054294884f2684b0f6c5a2", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "5751f6cd979e4227846c62ebd8e1d8a4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54ab2397ce054294884f2684b0f6c5a2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1e2a718f09084e9588daf833b1ae6265": {"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_624d4ea1aa284764a10bc726de6dc4ea", "IPY_MODEL_1f6c7668abad4ca086c193fa77dadb3d", "IPY_MODEL_39172253dc8f4e57b2cf7eac23a31705", "IPY_MODEL_b78a75ddc54246cfa94200550020dc93"], "layout": "IPY_MODEL_f49fa13835984c599954701ab3eadc3a"}}, "624d4ea1aa284764a10bc726de6dc4ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1a6ed418109644128ef44796b7409c88", "style": "IPY_MODEL_cd3c44e64e7a49a7a4e99063b64f567d", "value": "<h4 id=\"SingleValueinColumn_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\"><b>Single Value in Column</b></h4>"}}, "1a6ed418109644128ef44796b7409c88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd3c44e64e7a49a7a4e99063b64f567d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1f6c7668abad4ca086c193fa77dadb3d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db93c7a18e45441fa167eb9823de79df", "style": "IPY_MODEL_96a4a992e8454f488ad8d0b31c29740d", "value": "<p>Check if there are columns which have only a single unique value in all rows. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/data_integrity/plot_is_single_value.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "db93c7a18e45441fa167eb9823de79df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "96a4a992e8454f488ad8d0b31c29740d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "39172253dc8f4e57b2cf7eac23a31705": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_261542d7f0be43689f16d123d0c63391", "style": "IPY_MODEL_16014e4fcfe0454bb283f1d4a5be6bd2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_2dcaf_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2dcaf_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2dcaf_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2dcaf_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2dcaf_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2dcaf_\">\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_2dcaf_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2dcaf_row0_col1\" class=\"data row0 col1\" >Does not contain only a single value</td>\n      <td id=\"T_2dcaf_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"}}, "261542d7f0be43689f16d123d0c63391": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16014e4fcfe0454bb283f1d4a5be6bd2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b78a75ddc54246cfa94200550020dc93": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_806c87a666a849cba93cddfbd6d8fd09", "IPY_MODEL_11a1b25785544db4b600ebace165a923", "IPY_MODEL_a62cffaff79a44a9997d534c17595966", "IPY_MODEL_68450d73ea3e48ed8aa78757ae02f7cc"], "layout": "IPY_MODEL_f4d2b4e47995423eb49a7a1b38a34bed"}}, "806c87a666a849cba93cddfbd6d8fd09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58599b0bf0304488834deb69632033d9", "style": "IPY_MODEL_76b3b740967c4515a05e85d479baf567", "value": "<h5><b>Additional Outputs</b></h5>"}}, "58599b0bf0304488834deb69632033d9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "76b3b740967c4515a05e85d479baf567": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "11a1b25785544db4b600ebace165a923": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f2cd1eb4b92a430e96a689a12211779b", "style": "IPY_MODEL_165261b87a124fa4b495fe387447cd3e", "value": "<div>The following columns have only one unique value</div>"}}, "f2cd1eb4b92a430e96a689a12211779b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "165261b87a124fa4b495fe387447cd3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a62cffaff79a44a9997d534c17595966": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a012e64883bb43dd8cacd2288e40b078", "style": "IPY_MODEL_f34ae8185b9149d9b714ad00e7228633", "value": "<style type=\"text/css\">\n#T_522c6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_522c6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_522c6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_522c6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_522c6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_522c6_\">\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_522c6_level0_row0\" class=\"row_heading level0 row0\" >Single unique value</th>\n      <td id=\"T_522c6_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_522c6_row0_col1\" class=\"data row0 col1\" >False</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a012e64883bb43dd8cacd2288e40b078": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f34ae8185b9149d9b714ad00e7228633": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "68450d73ea3e48ed8aa78757ae02f7cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_965d780523f94be7951372b6b3555b5d", "style": "IPY_MODEL_524879bc597a4eaf947ae26619a30b8d", "value": "<br><a href=\"#summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\" style=\"font-size: 14px;\">Go to top</a>"}}, "965d780523f94be7951372b6b3555b5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "524879bc597a4eaf947ae26619a30b8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f4d2b4e47995423eb49a7a1b38a34bed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f49fa13835984c599954701ab3eadc3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82e31c4debdb4fd0b586a58725bbeeeb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8bed7986b09f4509b6f18825b5e17bea", "style": "IPY_MODEL_5c4084efe82349fbb64a605ad745dfb3", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8bed7986b09f4509b6f18825b5e17bea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5c4084efe82349fbb64a605ad745dfb3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0213ff2c340a48f5b222c6433471a07f": {"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_3f8d452f2d9d49cba5d100350a3b5d3d", "IPY_MODEL_6be43073437f4edab1d3197deb36f95d", "IPY_MODEL_348bcb0a7d234dd69f1e46276b0e46c6", "IPY_MODEL_8f3fc026c4a54e66bc43e187074cd34c"], "layout": "IPY_MODEL_00d54d4aa3b54de2a3ac32b225a1ea33"}}, "3f8d452f2d9d49cba5d100350a3b5d3d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e3627c8526ae49819d60c624cebf365b", "style": "IPY_MODEL_eab904d5d1ec44f4bdd09a373da27984", "value": "<h4 id=\"Feature-FeatureCorrelation_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\"><b>Feature-Feature Correlation</b></h4>"}}, "e3627c8526ae49819d60c624cebf365b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eab904d5d1ec44f4bdd09a373da27984": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6be43073437f4edab1d3197deb36f95d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c4637f5f402d4848a961891710b72683", "style": "IPY_MODEL_bacdc9db99cc43748b4191c1ec4b10c3", "value": "<p>    Checks for pairwise correlation between the features. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/data_integrity/plot_feature_feature_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c4637f5f402d4848a961891710b72683": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bacdc9db99cc43748b4191c1ec4b10c3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "348bcb0a7d234dd69f1e46276b0e46c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_78b4cd905b7147cc8d55f13001588ed7", "style": "IPY_MODEL_b36f97f8091141c2b50668f90fea6361", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6faed_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6faed_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6faed_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6faed_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6faed_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6faed_\">\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_6faed_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_6faed_row0_col1\" class=\"data row0 col1\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_6faed_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"}}, "78b4cd905b7147cc8d55f13001588ed7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b36f97f8091141c2b50668f90fea6361": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8f3fc026c4a54e66bc43e187074cd34c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_36b15e362e7145758bac361dad9bd587", "IPY_MODEL_28280579ecad4aafadde1ec92b6e8ed9", "IPY_MODEL_55ec7b6c9c2d41b7940404f8c0dd41e9", "IPY_MODEL_66cc4a9dfbd249cd95c78c0ecb99244d", "IPY_MODEL_57535a1c98214ff49135d32c23297c1d", "IPY_MODEL_37670681b04b4d1ea04236e495d6fd3d"], "layout": "IPY_MODEL_339e8748f91c4ee59ef955a6ace77dc4"}}, "36b15e362e7145758bac361dad9bd587": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", 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"model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82078f988a28444c91b3f21715e076f0", "style": "IPY_MODEL_a79a8f40665d4fa896101b6b07633622", "value": "<div>* Displayed as absolute values.</div>"}}, "82078f988a28444c91b3f21715e076f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a79a8f40665d4fa896101b6b07633622": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "66cc4a9dfbd249cd95c78c0ecb99244d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0cc2f58ae6fd4558b36bb374f7550330", "style": "IPY_MODEL_7b10c70dbb95440cbbd307465be0c687", "value": "<div>* Some features in the dataset are neither numerical nor categorical and therefore not calculated.</div>"}}, "0cc2f58ae6fd4558b36bb374f7550330": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7b10c70dbb95440cbbd307465be0c687": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57535a1c98214ff49135d32c23297c1d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c00302736e3b49af8fab6be015b93a62", "style": "IPY_MODEL_95756c57e609420c969d191aa233f3c2", "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>"}}, "c00302736e3b49af8fab6be015b93a62": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95756c57e609420c969d191aa233f3c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "37670681b04b4d1ea04236e495d6fd3d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f516dfc4cbaa4200b9102662a8faf6c4", "style": "IPY_MODEL_d8f0fc0dd969471eb99163ad589c84ab", "value": "<br><a href=\"#summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\" style=\"font-size: 14px;\">Go to top</a>"}}, "f516dfc4cbaa4200b9102662a8faf6c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8f0fc0dd969471eb99163ad589c84ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "339e8748f91c4ee59ef955a6ace77dc4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "00d54d4aa3b54de2a3ac32b225a1ea33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43422c4af42d4bbb94cddb2207382d77": {"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_23439b17654a40ca96711c37e7295b0c", "IPY_MODEL_e5b0b22ba09f4ceab5ed1019affdc6b0", "IPY_MODEL_e5bb7a090f3f4c62b384fc17818516f8", "IPY_MODEL_ed19f6295d3e4021af6d530228283690"], "layout": "IPY_MODEL_c3fa5b6b93fa48dbae95b95cc97cf825"}}, "23439b17654a40ca96711c37e7295b0c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1f3373cb93df4847969b1b6f911bac79", "style": "IPY_MODEL_311648f6e7564af0bcaef62336619ac8", "value": "<h4 id=\"DataDuplicates_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\"><b>Data Duplicates</b></h4>"}}, "1f3373cb93df4847969b1b6f911bac79": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "311648f6e7564af0bcaef62336619ac8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5b0b22ba09f4ceab5ed1019affdc6b0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ca45685d7261450fb8aa6ee334b952e9", "style": "IPY_MODEL_0079720c004b43158f5d42cbc026d9c1", "value": "<p>Checks for duplicate samples in the dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/data_integrity/plot_data_duplicates.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ca45685d7261450fb8aa6ee334b952e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0079720c004b43158f5d42cbc026d9c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5bb7a090f3f4c62b384fc17818516f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_853d17fd8fc44d3583b269a5d131b7bc", "style": "IPY_MODEL_6f3df0bd56644746af67b4c529f9efb7", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e9beb_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e9beb_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e9beb_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e9beb_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e9beb_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e9beb_\">\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_e9beb_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_e9beb_row0_col1\" class=\"data row0 col1\" >Duplicate data ratio is less or equal to 0%</td>\n      <td id=\"T_e9beb_row0_col2\" class=\"data row0 col2\" >Found 0.0088% duplicate data</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "853d17fd8fc44d3583b269a5d131b7bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f3df0bd56644746af67b4c529f9efb7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed19f6295d3e4021af6d530228283690": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_90fdb9c1cce44e39a6d80d56417799cd", "IPY_MODEL_30e8dc56edbf4bffbf28c528343fee14", "IPY_MODEL_12a1fb552ecd406a91d314384e47cb6f", "IPY_MODEL_ba0fd8125f7f4166998ae13fb780da4a", "IPY_MODEL_f7105a7799264241b41cd70cf4206991"], "layout": "IPY_MODEL_885f380a26f44030b0ae680ccdb06935"}}, "90fdb9c1cce44e39a6d80d56417799cd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e1ca8a72c41646afb9ecf4136593679d", "style": "IPY_MODEL_21efb4bf48484a21b60279774203ea68", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e1ca8a72c41646afb9ecf4136593679d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "21efb4bf48484a21b60279774203ea68": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "30e8dc56edbf4bffbf28c528343fee14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_02b311f918c0446c92bca91ac8f2daac", "style": "IPY_MODEL_4a4e79079ccb4e3aa08d1aa34e7d4c43", "value": "<div>0.0088% of data samples are duplicates. </div>"}}, "02b311f918c0446c92bca91ac8f2daac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4a4e79079ccb4e3aa08d1aa34e7d4c43": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12a1fb552ecd406a91d314384e47cb6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c85836c63b14b2fbd0cefc112f5f6e5", "style": "IPY_MODEL_dd6b6a2c14a841e7a1b716bb84ad6362", "value": "<div>Each row in the table shows an example of duplicate data and the number of times it appears.</div>"}}, "1c85836c63b14b2fbd0cefc112f5f6e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd6b6a2c14a841e7a1b716bb84ad6362": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ba0fd8125f7f4166998ae13fb780da4a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_43020020638949568f019ad26e4d576e", "style": "IPY_MODEL_90b214980f7f4dff977fda964ee36818", "value": "<style type=\"text/css\">\n#T_7b0a0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b0a0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b0a0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b0a0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b0a0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7b0a0_\">\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_7b0a0_level0_row0\" class=\"row_heading level0 row0\" >4696, 4719</th>\n      <th id=\"T_7b0a0_level1_row0\" class=\"row_heading level1 row0\" >2</th>\n      <td id=\"T_7b0a0_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_7b0a0_row0_col1\" class=\"data row0 col1\" >6</td>\n      <td id=\"T_7b0a0_row0_col2\" class=\"data row0 col2\" >2019-06-06</td>\n      <td id=\"T_7b0a0_row0_col3\" class=\"data row0 col3\" >other</td>\n      <td id=\"T_7b0a0_row0_col4\" class=\"data row0 col4\" >123</td>\n      <td id=\"T_7b0a0_row0_col5\" class=\"data row0 col5\" >28</td>\n      <td id=\"T_7b0a0_row0_col6\" class=\"data row0 col6\" >4</td>\n      <td id=\"T_7b0a0_row0_col7\" class=\"data row0 col7\" >0</td>\n      <td id=\"T_7b0a0_row0_col8\" class=\"data row0 col8\" >0</td>\n      <td id=\"T_7b0a0_row0_col9\" class=\"data row0 col9\" >-4.91</td>\n      <td id=\"T_7b0a0_row0_col10\" class=\"data row0 col10\" >0</td>\n      <td id=\"T_7b0a0_row0_col11\" class=\"data row0 col11\" >True</td>\n      <td id=\"T_7b0a0_row0_col12\" class=\"data row0 col12\" >False</td>\n      <td id=\"T_7b0a0_row0_col13\" class=\"data row0 col13\" >False</td>\n      <td id=\"T_7b0a0_row0_col14\" class=\"data row0 col14\" >0</td>\n      <td id=\"T_7b0a0_row0_col15\" class=\"data row0 col15\" >0</td>\n      <td id=\"T_7b0a0_row0_col16\" class=\"data row0 col16\" >0</td>\n      <td id=\"T_7b0a0_row0_col17\" class=\"data row0 col17\" >0</td>\n      <td id=\"T_7b0a0_row0_col18\" class=\"data row0 col18\" >0</td>\n      <td id=\"T_7b0a0_row0_col19\" class=\"data row0 col19\" >0</td>\n      <td id=\"T_7b0a0_row0_col20\" class=\"data row0 col20\" >0</td>\n      <td id=\"T_7b0a0_row0_col21\" class=\"data row0 col21\" >0</td>\n      <td id=\"T_7b0a0_row0_col22\" class=\"data row0 col22\" >0.00</td>\n      <td id=\"T_7b0a0_row0_col23\" class=\"data row0 col23\" >0.00</td>\n      <td id=\"T_7b0a0_row0_col24\" class=\"data row0 col24\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "43020020638949568f019ad26e4d576e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90b214980f7f4dff977fda964ee36818": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f7105a7799264241b41cd70cf4206991": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f0a77eaab674ebc8a61c805ae663552", "style": "IPY_MODEL_f3a13547b7ed4cea8495a21dae85cb0e", "value": "<br><a href=\"#summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-NOFY6\" style=\"font-size: 14px;\">Go to top</a>"}}, "3f0a77eaab674ebc8a61c805ae663552": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f3a13547b7ed4cea8495a21dae85cb0e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "885f380a26f44030b0ae680ccdb06935": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3fa5b6b93fa48dbae95b95cc97cf825": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ccd526eed43d45bdb8660c57b90f0ee5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df0c576ff36a43e2ac62ee4c7d77794e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2ed30b9b51bb41aa9b88499ff919750e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e7ec5295231c423694cf22f7ddd90d11": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ae13485002b64212bb76c580b8ba28f1", "IPY_MODEL_cf0bf6c153844c07b0472ac24b76d626"], "layout": "IPY_MODEL_d5058f9c388e47dda44fd8b09a11cd84"}}, "ae13485002b64212bb76c580b8ba28f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5694772dfd5c4fda9a5547252ddbe360", "style": "IPY_MODEL_dc7afb4e28a442058f641a166f56d778", "value": "<span id=\"summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-452H2\"></span>"}}, "5694772dfd5c4fda9a5547252ddbe360": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc7afb4e28a442058f641a166f56d778": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cf0bf6c153844c07b0472ac24b76d626": {"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_985995a054524156b5132aa57e470dc0"], "layout": "IPY_MODEL_467016e26af240d5b6694b61fc98d3e1", "selected_index": null}}, "985995a054524156b5132aa57e470dc0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2149eb76449c44939698759da560b60a", "IPY_MODEL_1bf21f2a315d43eaabb027740623c1ab", "IPY_MODEL_968d6145a9c04a069aaecd55d243f597"], "layout": "IPY_MODEL_605070fe749d475291252fe8b3f688d1"}}, "2149eb76449c44939698759da560b60a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_20e2d7e12a16411a8c0ea5e93df25206", "style": "IPY_MODEL_5663a383bf87415bbe8ca7abfb663717", "value": "<style type=\"text/css\">\n#T_220da_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_220da_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_220da_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_220da_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_220da_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_220da_\">\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_220da_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row0_col1\" class=\"data row0 col1\" ><a href=\"#FeatureLabelCorrelation_LPYS280XKXWD8IL3FB9W1Q5UZ-section-452H2\">Feature Label Correlation</a></td>\n      <td id=\"T_220da_row0_col2\" class=\"data row0 col2\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_220da_row0_col3\" class=\"data row0 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_220da_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row1_col1\" class=\"data row1 col1\" >Special Characters</td>\n      <td id=\"T_220da_row1_col2\" class=\"data row1 col2\" >Ratio of samples containing solely special character is less or equal to 0.1%</td>\n      <td id=\"T_220da_row1_col3\" class=\"data row1 col3\" >Passed for 25 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_220da_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row2_col1\" class=\"data row2 col1\" >Mixed Nulls</td>\n      <td id=\"T_220da_row2_col2\" class=\"data row2 col2\" >Number of different null types is less or equal to 1</td>\n      <td id=\"T_220da_row2_col3\" class=\"data row2 col3\" >Passed for 25 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_220da_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row3_col1\" class=\"data row3 col1\" >Mixed Data Types</td>\n      <td id=\"T_220da_row3_col2\" class=\"data row3 col2\" >Rare data types in column are either more than 10% or less than 1% of the data</td>\n      <td id=\"T_220da_row3_col3\" class=\"data row3 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_220da_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row4_col1\" class=\"data row4 col1\" >String Mismatch</td>\n      <td id=\"T_220da_row4_col2\" class=\"data row4 col2\" >No string variants</td>\n      <td id=\"T_220da_row4_col3\" class=\"data row4 col3\" >Passed for 2 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_220da_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row5_col1\" class=\"data row5 col1\" >String Length Out Of Bounds</td>\n      <td id=\"T_220da_row5_col2\" class=\"data row5 col2\" >Ratio of string length outliers is less or equal to 0%</td>\n      <td id=\"T_220da_row5_col3\" class=\"data row5 col3\" >Passed for 2 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_220da_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row6_col1\" class=\"data row6 col1\" >Conflicting Labels</td>\n      <td id=\"T_220da_row6_col2\" class=\"data row6 col2\" >Ambiguous sample ratio is less or equal to 0%</td>\n      <td id=\"T_220da_row6_col3\" class=\"data row6 col3\" >Ratio of samples with conflicting labels: 0%</td>\n    </tr>\n    <tr>\n      <td id=\"T_220da_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_220da_row7_col1\" class=\"data row7 col1\" >Identifier Label Correlation</td>\n      <td id=\"T_220da_row7_col2\" class=\"data row7 col2\" >Identifier columns PPS is less or equal to 0</td>\n      <td id=\"T_220da_row7_col3\" class=\"data row7 col3\" >Passed for 1 relevant column</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "20e2d7e12a16411a8c0ea5e93df25206": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5663a383bf87415bbe8ca7abfb663717": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1bf21f2a315d43eaabb027740623c1ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_78380acca6f748c59ec6e49847b89875", "style": "IPY_MODEL_12bafbc2120041fa94e83a1757c4e860", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "78380acca6f748c59ec6e49847b89875": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "12bafbc2120041fa94e83a1757c4e860": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "968d6145a9c04a069aaecd55d243f597": {"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_3b6575cc56224e1396f676f1782d098d", "IPY_MODEL_95b2101810644f81a1fd15fff3f5133c", "IPY_MODEL_490dab44336f48e8a2de9a0aa038abc7", "IPY_MODEL_132eebfd41aa413d84db391e2e6d2b42"], "layout": "IPY_MODEL_a8a48a662fca4deabcc36d83883e9105"}}, "3b6575cc56224e1396f676f1782d098d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d709c10ad2cc409283848d77c13b61a2", "style": "IPY_MODEL_c2ea4679bf674733a29e580c490706ed", "value": "<h4 id=\"FeatureLabelCorrelation_LPYS280XKXWD8IL3FB9W1Q5UZ-section-452H2\"><b>Feature Label Correlation</b></h4>"}}, "d709c10ad2cc409283848d77c13b61a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2ea4679bf674733a29e580c490706ed": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "95b2101810644f81a1fd15fff3f5133c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1cef2637fc17448eae8a05f34f902fab", "style": "IPY_MODEL_74fc8fd7342d40938c31fb381633d31f", "value": "<p>Return the PPS (Predictive Power Score) of all features in relation to the label. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/data_integrity/plot_feature_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1cef2637fc17448eae8a05f34f902fab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "74fc8fd7342d40938c31fb381633d31f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "490dab44336f48e8a2de9a0aa038abc7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_19d49cd5488d4df79f9ffaaf9d37fa8b", "style": "IPY_MODEL_8be37216fce94d388a95ea773dcdbcb4", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_45445_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_45445_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_45445_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_45445_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_45445_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_45445_\">\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_45445_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_45445_row0_col1\" class=\"data row0 col1\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_45445_row0_col2\" class=\"data row0 col2\" >Passed for 24 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "19d49cd5488d4df79f9ffaaf9d37fa8b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8be37216fce94d388a95ea773dcdbcb4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "132eebfd41aa413d84db391e2e6d2b42": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ecf91446fdd0434199969d1e26d6dc47", "IPY_MODEL_02295f407179409c860f5af625170b23", "IPY_MODEL_9dc3bc364386404c802f8801a437454f", "IPY_MODEL_79af1aa903ae4404a1d721d878d32c5e"], "layout": "IPY_MODEL_6263f4823e60495589a3bff346ccf16a"}}, "ecf91446fdd0434199969d1e26d6dc47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_60f557f10d644d17b2de571445bad26d", "style": "IPY_MODEL_2a739993d1de46228999ef69a1cf127a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "60f557f10d644d17b2de571445bad26d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a739993d1de46228999ef69a1cf127a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02295f407179409c860f5af625170b23": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.3</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["urlLength", "month", "scrape_date", "ext", "numDigits"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "7e155a86-2d9d-4857-941c-be7a9a7c2e0f"}], "_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, 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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": "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": "YvOkKc5o0z8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="}]}, "9dc3bc364386404c802f8801a437454f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_78cc6a83276e464497bdb45f229d6f09", "style": "IPY_MODEL_355e848126614e10b52b196d1b4157a0", "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/en/stable/checks_gallery/tabular/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>"}}, "78cc6a83276e464497bdb45f229d6f09": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "355e848126614e10b52b196d1b4157a0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79af1aa903ae4404a1d721d878d32c5e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a2cac902f44744cfa6756041a0bb5a8d", "style": "IPY_MODEL_ef858cef90e34e2baf03a00c32dbdc53", "value": "<br><a href=\"#summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-452H2\" style=\"font-size: 14px;\">Go to top</a>"}}, "a2cac902f44744cfa6756041a0bb5a8d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef858cef90e34e2baf03a00c32dbdc53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6263f4823e60495589a3bff346ccf16a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8a48a662fca4deabcc36d83883e9105": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "605070fe749d475291252fe8b3f688d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "467016e26af240d5b6694b61fc98d3e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5058f9c388e47dda44fd8b09a11cd84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14025001d683470096230fdd4307f25e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d966ea4666b942dbbc6e5ad839ae0971", "IPY_MODEL_4c330bc6c5a749e2b5063af94f6c8dff"], "layout": "IPY_MODEL_dffd3152803b4c86b973a6d471852781"}}, "d966ea4666b942dbbc6e5ad839ae0971": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_582737b17df748db8aec60d66ca7b136", "style": "IPY_MODEL_9d42eec0104840bb8fe25b2012a7045a", "value": "<span id=\"summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-ZQU7E\"></span>"}}, "582737b17df748db8aec60d66ca7b136": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d42eec0104840bb8fe25b2012a7045a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4c330bc6c5a749e2b5063af94f6c8dff": {"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_f6f635998af545d6a880025202a47cb1"], "layout": "IPY_MODEL_66728606705c4d2086b7afe2889f12e9", "selected_index": null}}, "f6f635998af545d6a880025202a47cb1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d8b52c4728a7422385a73da699218ae2", "IPY_MODEL_35f713e8696940bdadf639181dbf8df5", "IPY_MODEL_524f8d43d5d8456cbfdb8e9738828393"], "layout": "IPY_MODEL_137540d076d94ff8b9e9e3012abd61ce"}}, "d8b52c4728a7422385a73da699218ae2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_512a4a1d2e184cef9ab1e27d217dd98e", "style": "IPY_MODEL_dd9641c69e1a4f5aaa3187bd459a1e85", "value": "<style type=\"text/css\">\n#T_72177_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72177_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72177_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72177_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72177_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_72177_\">\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_72177_row0_col0\" class=\"data row0 col0\" ><a href=\"#OutlierSampleDetection_LPYS280XKXWD8IL3FB9W1Q5UZ-section-ZQU7E\">Outlier Sample Detection</a></td>\n      <td id=\"T_72177_row0_col1\" class=\"data row0 col1\" >Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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"}}, "512a4a1d2e184cef9ab1e27d217dd98e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd9641c69e1a4f5aaa3187bd459a1e85": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "35f713e8696940bdadf639181dbf8df5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8eec5960f696468d9d4dd2624339e785", "style": "IPY_MODEL_164c2e27aa2546ee830ace238af4de10", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8eec5960f696468d9d4dd2624339e785": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "164c2e27aa2546ee830ace238af4de10": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "524f8d43d5d8456cbfdb8e9738828393": {"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_eb1cdebb249b466f9a12a2b51de8e70e", "IPY_MODEL_76624267d3f24161825e0799375c1115", "IPY_MODEL_1bb2af2bffd7496e80e1cea5de2aea01"], "layout": "IPY_MODEL_6cf65ce8f1e549eea08258e494500671"}}, "eb1cdebb249b466f9a12a2b51de8e70e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b6476b2dc8a442708f554d3b9dba9b56", "style": "IPY_MODEL_0e6c38f7044d4c8692ff7ba446a9b314", "value": "<h4 id=\"OutlierSampleDetection_LPYS280XKXWD8IL3FB9W1Q5UZ-section-ZQU7E\"><b>Outlier Sample Detection</b></h4>"}}, "b6476b2dc8a442708f554d3b9dba9b56": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e6c38f7044d4c8692ff7ba446a9b314": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "76624267d3f24161825e0799375c1115": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dfb1f79c7aa6442fb5c5bcdc1675011e", "style": "IPY_MODEL_1b39429a9d1d42d78872ec9843b33647", "value": "<p>Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/data_integrity/plot_outlier_sample_detection.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "dfb1f79c7aa6442fb5c5bcdc1675011e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b39429a9d1d42d78872ec9843b33647": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1bb2af2bffd7496e80e1cea5de2aea01": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4e29c14a88614651bf0ac4fbb66592d0", "IPY_MODEL_028b0017446748539ca6758e434358d6", "IPY_MODEL_362dac36fc1b444fb237fa6f16ce7a14", "IPY_MODEL_e2ec13db3fcf400789c5728cbc49f1d4", "IPY_MODEL_f705ecd0864c464faa7ea073868f2587"], "layout": "IPY_MODEL_53e274fa012044fe8c0091e22823e262"}}, "4e29c14a88614651bf0ac4fbb66592d0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d948d6838d5e4dc2b422aeb338b86d56", "style": "IPY_MODEL_9aea7039bc9e4a018590de6fdfc9e5ed", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d948d6838d5e4dc2b422aeb338b86d56": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9aea7039bc9e4a018590de6fdfc9e5ed": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "028b0017446748539ca6758e434358d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c5be34a015d4403b779df7166f4e655", "style": "IPY_MODEL_692394e2cf784ceda3e488c909c09920", "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>"}}, "9c5be34a015d4403b779df7166f4e655": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "692394e2cf784ceda3e488c909c09920": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "362dac36fc1b444fb237fa6f16ce7a14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_75a9fb1d4cc440dbb897ff5294badfe7", "style": "IPY_MODEL_4dfbcd6ffaa340faa5be66f178a8640a", "value": "<style type=\"text/css\">\n#T_2238c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2238c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2238c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2238c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2238c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2238c_\">\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_2238c_level0_row0\" class=\"row_heading level0 row0\" >4129</th>\n      <td id=\"T_2238c_row0_col0\" class=\"data row0 col0\" >1.00</td>\n      <td id=\"T_2238c_row0_col1\" class=\"data row0 col1\" >0</td>\n      <td id=\"T_2238c_row0_col2\" class=\"data row0 col2\" >5</td>\n      <td id=\"T_2238c_row0_col3\" class=\"data row0 col3\" >2019-05-18</td>\n      <td id=\"T_2238c_row0_col4\" class=\"data row0 col4\" >country</td>\n      <td id=\"T_2238c_row0_col5\" class=\"data row0 col5\" >128</td>\n      <td id=\"T_2238c_row0_col6\" class=\"data row0 col6\" >34</td>\n      <td id=\"T_2238c_row0_col7\" class=\"data row0 col7\" >0</td>\n      <td id=\"T_2238c_row0_col8\" class=\"data row0 col8\" >10</td>\n      <td id=\"T_2238c_row0_col9\" class=\"data row0 col9\" >0</td>\n      <td id=\"T_2238c_row0_col10\" class=\"data row0 col10\" >-4.51</td>\n      <td id=\"T_2238c_row0_col11\" class=\"data row0 col11\" >0</td>\n      <td id=\"T_2238c_row0_col12\" class=\"data row0 col12\" >True</td>\n      <td id=\"T_2238c_row0_col13\" class=\"data row0 col13\" >False</td>\n      <td id=\"T_2238c_row0_col14\" class=\"data row0 col14\" >False</td>\n      <td id=\"T_2238c_row0_col15\" class=\"data row0 col15\" >0</td>\n      <td id=\"T_2238c_row0_col16\" class=\"data row0 col16\" >0</td>\n      <td id=\"T_2238c_row0_col17\" class=\"data row0 col17\" >31701</td>\n      <td id=\"T_2238c_row0_col18\" class=\"data row0 col18\" >0</td>\n      <td id=\"T_2238c_row0_col19\" class=\"data row0 col19\" >4</td>\n      <td id=\"T_2238c_row0_col20\" class=\"data row0 col20\" >39</td>\n      <td id=\"T_2238c_row0_col21\" class=\"data row0 col21\" >5420</td>\n      <td id=\"T_2238c_row0_col22\" class=\"data row0 col22\" >794</td>\n      <td id=\"T_2238c_row0_col23\" class=\"data row0 col23\" >0.03</td>\n      <td id=\"T_2238c_row0_col24\" class=\"data row0 col24\" >0.17</td>\n      <td id=\"T_2238c_row0_col25\" class=\"data row0 col25\" >0.12</td>\n    </tr>\n    <tr>\n      <th id=\"T_2238c_level0_row1\" class=\"row_heading level0 row1\" >6505</th>\n      <td id=\"T_2238c_row1_col0\" class=\"data row1 col0\" >1.00</td>\n      <td id=\"T_2238c_row1_col1\" class=\"data row1 col1\" >0</td>\n      <td id=\"T_2238c_row1_col2\" class=\"data row1 col2\" >8</td>\n      <td id=\"T_2238c_row1_col3\" class=\"data row1 col3\" >2019-08-05</td>\n      <td id=\"T_2238c_row1_col4\" class=\"data row1 col4\" >html</td>\n      <td id=\"T_2238c_row1_col5\" class=\"data row1 col5\" >94</td>\n      <td id=\"T_2238c_row1_col6\" class=\"data row1 col6\" >7</td>\n      <td id=\"T_2238c_row1_col7\" class=\"data row1 col7\" >2</td>\n      <td id=\"T_2238c_row1_col8\" class=\"data row1 col8\" >0</td>\n      <td id=\"T_2238c_row1_col9\" class=\"data row1 col9\" >0</td>\n      <td id=\"T_2238c_row1_col10\" class=\"data row1 col10\" >-4.61</td>\n      <td id=\"T_2238c_row1_col11\" class=\"data row1 col11\" >0</td>\n      <td id=\"T_2238c_row1_col12\" class=\"data row1 col12\" >True</td>\n      <td id=\"T_2238c_row1_col13\" class=\"data row1 col13\" >False</td>\n      <td id=\"T_2238c_row1_col14\" class=\"data row1 col14\" >False</td>\n      <td id=\"T_2238c_row1_col15\" class=\"data row1 col15\" >0</td>\n      <td id=\"T_2238c_row1_col16\" class=\"data row1 col16\" >0</td>\n      <td id=\"T_2238c_row1_col17\" class=\"data row1 col17\" >282</td>\n      <td id=\"T_2238c_row1_col18\" class=\"data row1 col18\" >0</td>\n      <td id=\"T_2238c_row1_col19\" class=\"data row1 col19\" >2</td>\n      <td id=\"T_2238c_row1_col20\" class=\"data row1 col20\" >2</td>\n      <td id=\"T_2238c_row1_col21\" class=\"data row1 col21\" >167</td>\n      <td id=\"T_2238c_row1_col22\" class=\"data row1 col22\" >0</td>\n      <td id=\"T_2238c_row1_col23\" class=\"data row1 col23\" >0.00</td>\n      <td id=\"T_2238c_row1_col24\" class=\"data row1 col24\" >0.59</td>\n      <td id=\"T_2238c_row1_col25\" class=\"data row1 col25\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_2238c_level0_row2\" class=\"row_heading level0 row2\" >2036</th>\n      <td id=\"T_2238c_row2_col0\" class=\"data row2 col0\" >0.99</td>\n      <td id=\"T_2238c_row2_col1\" class=\"data row2 col1\" >0</td>\n      <td id=\"T_2238c_row2_col2\" class=\"data row2 col2\" >3</td>\n      <td id=\"T_2238c_row2_col3\" class=\"data row2 col3\" >2019-03-08</td>\n      <td id=\"T_2238c_row2_col4\" class=\"data row2 col4\" >com</td>\n      <td id=\"T_2238c_row2_col5\" class=\"data row2 col5\" >103</td>\n      <td id=\"T_2238c_row2_col6\" class=\"data row2 col6\" >5</td>\n      <td id=\"T_2238c_row2_col7\" class=\"data row2 col7\" >0</td>\n      <td id=\"T_2238c_row2_col8\" class=\"data row2 col8\" >0</td>\n      <td id=\"T_2238c_row2_col9\" class=\"data row2 col9\" >0</td>\n      <td id=\"T_2238c_row2_col10\" class=\"data row2 col10\" >-4.27</td>\n      <td id=\"T_2238c_row2_col11\" class=\"data row2 col11\" >0</td>\n      <td id=\"T_2238c_row2_col12\" class=\"data row2 col12\" >True</td>\n      <td id=\"T_2238c_row2_col13\" class=\"data row2 col13\" >False</td>\n      <td id=\"T_2238c_row2_col14\" class=\"data row2 col14\" >False</td>\n      <td id=\"T_2238c_row2_col15\" class=\"data row2 col15\" >5469</td>\n      <td id=\"T_2238c_row2_col16\" class=\"data row2 col16\" >374</td>\n      <td id=\"T_2238c_row2_col17\" class=\"data row2 col17\" >43944</td>\n      <td id=\"T_2238c_row2_col18\" class=\"data row2 col18\" >1042</td>\n      <td id=\"T_2238c_row2_col19\" class=\"data row2 col19\" >526</td>\n      <td id=\"T_2238c_row2_col20\" class=\"data row2 col20\" >73</td>\n      <td id=\"T_2238c_row2_col21\" class=\"data row2 col21\" >9730</td>\n      <td id=\"T_2238c_row2_col22\" class=\"data row2 col22\" >2441</td>\n      <td id=\"T_2238c_row2_col23\" class=\"data row2 col23\" >0.06</td>\n      <td id=\"T_2238c_row2_col24\" class=\"data row2 col24\" >0.22</td>\n      <td id=\"T_2238c_row2_col25\" class=\"data row2 col25\" >0.25</td>\n    </tr>\n    <tr>\n      <th id=\"T_2238c_level0_row3\" class=\"row_heading level0 row3\" >1486</th>\n      <td id=\"T_2238c_row3_col0\" class=\"data row3 col0\" >0.99</td>\n      <td id=\"T_2238c_row3_col1\" class=\"data row3 col1\" >0</td>\n      <td id=\"T_2238c_row3_col2\" class=\"data row3 col2\" >2</td>\n      <td id=\"T_2238c_row3_col3\" class=\"data row3 col3\" >2019-02-18</td>\n      <td id=\"T_2238c_row3_col4\" class=\"data row3 col4\" >other</td>\n      <td id=\"T_2238c_row3_col5\" class=\"data row3 col5\" >181</td>\n      <td id=\"T_2238c_row3_col6\" class=\"data row3 col6\" >20</td>\n      <td id=\"T_2238c_row3_col7\" class=\"data row3 col7\" >0</td>\n      <td id=\"T_2238c_row3_col8\" class=\"data row3 col8\" >0</td>\n      <td id=\"T_2238c_row3_col9\" class=\"data row3 col9\" >2</td>\n      <td id=\"T_2238c_row3_col10\" class=\"data row3 col10\" >-4.67</td>\n      <td id=\"T_2238c_row3_col11\" class=\"data row3 col11\" >0</td>\n      <td id=\"T_2238c_row3_col12\" class=\"data row3 col12\" >True</td>\n      <td id=\"T_2238c_row3_col13\" class=\"data row3 col13\" >False</td>\n      <td id=\"T_2238c_row3_col14\" class=\"data row3 col14\" >False</td>\n      <td id=\"T_2238c_row3_col15\" class=\"data row3 col15\" >0</td>\n      <td id=\"T_2238c_row3_col16\" class=\"data row3 col16\" >0</td>\n      <td id=\"T_2238c_row3_col17\" class=\"data row3 col17\" >0</td>\n      <td id=\"T_2238c_row3_col18\" class=\"data row3 col18\" >0</td>\n      <td id=\"T_2238c_row3_col19\" class=\"data row3 col19\" >0</td>\n      <td id=\"T_2238c_row3_col20\" class=\"data row3 col20\" >0</td>\n      <td id=\"T_2238c_row3_col21\" class=\"data row3 col21\" >0</td>\n      <td id=\"T_2238c_row3_col22\" class=\"data row3 col22\" >0</td>\n      <td id=\"T_2238c_row3_col23\" class=\"data row3 col23\" >0.00</td>\n      <td id=\"T_2238c_row3_col24\" class=\"data row3 col24\" >0.00</td>\n      <td id=\"T_2238c_row3_col25\" class=\"data row3 col25\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_2238c_level0_row4\" class=\"row_heading level0 row4\" >7773</th>\n      <td id=\"T_2238c_row4_col0\" class=\"data row4 col0\" >0.99</td>\n      <td id=\"T_2238c_row4_col1\" class=\"data row4 col1\" >1</td>\n      <td id=\"T_2238c_row4_col2\" class=\"data row4 col2\" >9</td>\n      <td id=\"T_2238c_row4_col3\" class=\"data row4 col3\" >2019-09-17</td>\n      <td id=\"T_2238c_row4_col4\" class=\"data row4 col4\" >country</td>\n      <td id=\"T_2238c_row4_col5\" class=\"data row4 col5\" >102</td>\n      <td id=\"T_2238c_row4_col6\" class=\"data row4 col6\" >0</td>\n      <td id=\"T_2238c_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_2238c_row4_col8\" class=\"data row4 col8\" >0</td>\n      <td id=\"T_2238c_row4_col9\" class=\"data row4 col9\" >0</td>\n      <td id=\"T_2238c_row4_col10\" class=\"data row4 col10\" >-4.62</td>\n      <td id=\"T_2238c_row4_col11\" class=\"data row4 col11\" >0</td>\n      <td id=\"T_2238c_row4_col12\" class=\"data row4 col12\" >True</td>\n      <td id=\"T_2238c_row4_col13\" class=\"data row4 col13\" >True</td>\n      <td id=\"T_2238c_row4_col14\" class=\"data row4 col14\" >False</td>\n      <td id=\"T_2238c_row4_col15\" class=\"data row4 col15\" >7800</td>\n      <td id=\"T_2238c_row4_col16\" class=\"data row4 col16\" >235</td>\n      <td id=\"T_2238c_row4_col17\" class=\"data row4 col17\" >0</td>\n      <td id=\"T_2238c_row4_col18\" class=\"data row4 col18\" >0</td>\n      <td id=\"T_2238c_row4_col19\" class=\"data row4 col19\" >0</td>\n      <td id=\"T_2238c_row4_col20\" class=\"data row4 col20\" >0</td>\n      <td id=\"T_2238c_row4_col21\" class=\"data row4 col21\" >0</td>\n      <td id=\"T_2238c_row4_col22\" class=\"data row4 col22\" >0</td>\n      <td id=\"T_2238c_row4_col23\" class=\"data row4 col23\" >0.00</td>\n      <td id=\"T_2238c_row4_col24\" class=\"data row4 col24\" >0.00</td>\n      <td id=\"T_2238c_row4_col25\" class=\"data row4 col25\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "75a9fb1d4cc440dbb897ff5294badfe7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4dfbcd6ffaa340faa5be66f178a8640a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2ec13db3fcf400789c5728cbc49f1d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8b9cdce1b70e491fb7c363d083014644", "style": "IPY_MODEL_1127d5aa4e8e411489ef4f8e5db24b00", "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. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "8b9cdce1b70e491fb7c363d083014644": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1127d5aa4e8e411489ef4f8e5db24b00": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f705ecd0864c464faa7ea073868f2587": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c71b5133b1f14236841d66e6d92906c7", "style": "IPY_MODEL_a3ba0156164a468e96770671f2019e65", "value": "<br><a href=\"#summary_LPYS280XKXWD8IL3FB9W1Q5UZ-section-ZQU7E\" style=\"font-size: 14px;\">Go to top</a>"}}, "c71b5133b1f14236841d66e6d92906c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a3ba0156164a468e96770671f2019e65": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "53e274fa012044fe8c0091e22823e262": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6cf65ce8f1e549eea08258e494500671": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "137540d076d94ff8b9e9e3012abd61ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "66728606705c4d2086b7afe2889f12e9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dffd3152803b4c86b973a6d471852781": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c8cfead37a244578835b93d92d252db": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2ddee48c0042495b85a712753ee40168", "IPY_MODEL_9184e7d229ea4051b1f9d23470b53afd"], "layout": "IPY_MODEL_395b6b9a284a4318a1722c4d3373478e"}}, "2ddee48c0042495b85a712753ee40168": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0058fc581a1a4502882e30a11201fde7", "style": "IPY_MODEL_3c134321187e41609dcacb3d23e68f78"}}, "0058fc581a1a4502882e30a11201fde7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c134321187e41609dcacb3d23e68f78": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9184e7d229ea4051b1f9d23470b53afd": {"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 Run"}, "children": ["IPY_MODEL_3628bc6dcc0a49f5a6c5ee20a3d42e06"], "layout": "IPY_MODEL_24b8ce6883a54b87ac7b57bdc2220596", "selected_index": null}}, "3628bc6dcc0a49f5a6c5ee20a3d42e06": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cbdeaf86122b46139153c5458c69df54", "style": "IPY_MODEL_7843cd2d71674cd7a27a7890c1c7bd01", "value": "<p>No outputs to show.</p>"}}, "cbdeaf86122b46139153c5458c69df54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7843cd2d71674cd7a27a7890c1c7bd01": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "24b8ce6883a54b87ac7b57bdc2220596": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "395b6b9a284a4318a1722c4d3373478e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "455cdd55cd5f411dbd2d5c1cd1144d1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f550f1541d34fb1a220915e759375bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
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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>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </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>
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          <td>2.299385</td>
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          <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>
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          <td>0.953338</td>
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        </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">
    <div>
    <style scoped>
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            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>
          <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>
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        <tr>
          <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>
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          <td>0.153424</td>
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        <tr>
          <th>2019-10-01</th>
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          <td>0.342233</td>
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          <td>-0.089699</td>
          <td>-0.068846</td>
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          <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=[])








.. 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:
    |######      | 6/12 [Time: 00:00, Check=Date Train Test Leakage Overlap]
    Train Test Validation Suite:
    |############| 12/12 [Time: 00:03, Check=Multivariate Drift]            
                                                                                         

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    {"version_major": 2, "version_minor": 0, "state": {"9c8aed5a2ab746468b7d445041fac56c": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Train Test Validation Suite"}, "children": ["IPY_MODEL_da42d0cadd984396a2cbd0ad158afb72"], "layout": "IPY_MODEL_a6c53e6c1a3443c8a8f3c3e5359c8fd8"}}, "da42d0cadd984396a2cbd0ad158afb72": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5ea9d63ffa5d4bdebf251561afababa9", "IPY_MODEL_66f08c8639794e6c926bcd43fa1c0a26", "IPY_MODEL_fc3b1e512b184493aca343aa5ea7e423", "IPY_MODEL_de123a5b9d3e4f438fc949a75d0939f4", "IPY_MODEL_7332f05271664101a621adca4a484298"], "layout": "IPY_MODEL_8698335346ff418d9abcff27ef8d9a48"}}, "5ea9d63ffa5d4bdebf251561afababa9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_caae7a2ded2347679ee61b175e372ce1", "style": "IPY_MODEL_586b18c048eb4430a9104efa5c73e49c", "value": "\n            <h1 id=\"summary_EQTUL1P36P6QF5Q14J1RIPZIM\">Train Test Validation Suite</h1>\n            <p>\n                The suite is composed of various checks such as: Train Test Samples Mix, New Label Train Test, Index Train Test Leakage, etc...<br>\n                Each check may contain conditions (which will result in pass / fail / warning / error\n                , represented by \n<span style=\"color: green;display:inline-block\">\u2713</span> /\n<span style=\"color: red;display:inline-block\">\u2716</span> /\n<span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> /\n<span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>\n) as well as other outputs such as plots or tables.<br>\n                Suites, checks and conditions can all be modified. Read more about\n                <a href=https://docs.deepchecks.com/en/stable/examples/guides/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"}}, "caae7a2ded2347679ee61b175e372ce1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "586b18c048eb4430a9104efa5c73e49c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "66f08c8639794e6c926bcd43fa1c0a26": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_77d9d8e308b54e3dbfd56095879a79e1", "IPY_MODEL_5dcef31187c841c38a0d4e18fc7d4d9e"], "layout": "IPY_MODEL_38c8cbb72c864b5695e90a84e2ce078f"}}, "77d9d8e308b54e3dbfd56095879a79e1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e031739782b4c768cf7541fadcf66ab", "style": "IPY_MODEL_032baf4567df46bcacbe7bebef04426a", "value": "<span id=\"summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-6CFNF\"></span>"}}, "3e031739782b4c768cf7541fadcf66ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "032baf4567df46bcacbe7bebef04426a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5dcef31187c841c38a0d4e18fc7d4d9e": {"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_56828fdf1c3d47b58a0eac91601a78a7"], "layout": "IPY_MODEL_ff7227e385c748919352e00ff5304a71", "selected_index": null}}, "56828fdf1c3d47b58a0eac91601a78a7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2be00396c28243ffa787c5d4cdd1bee5", "IPY_MODEL_908a2252406a4c229d55596c6accaad0", "IPY_MODEL_0b0c039f2c184770af205dd08ab2b5ee"], "layout": "IPY_MODEL_398cdf8bdc0b4b11b0f516a53097cb5d"}}, "2be00396c28243ffa787c5d4cdd1bee5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6605cd971caa4f77b1c87dfcb3b857a1", "style": "IPY_MODEL_5d2619d5ad3a477fa981b42f37c9c0f7", "value": "<style type=\"text/css\">\n#T_711d2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_711d2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_711d2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_711d2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_711d2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_711d2_\">\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_711d2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_711d2_row0_col1\" class=\"data row0 col1\" ><a href=\"#DateTrain-TestLeakage(overlap)_EQTUL1P36P6QF5Q14J1RIPZIM-section-6CFNF\">Date Train-Test Leakage (overlap)</a></td>\n      <td id=\"T_711d2_row0_col2\" class=\"data row0 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_711d2_row0_col3\" class=\"data row0 col3\" >Found 100% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6605cd971caa4f77b1c87dfcb3b857a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d2619d5ad3a477fa981b42f37c9c0f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "908a2252406a4c229d55596c6accaad0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c4f826fe2ffa4a63b0302c658c88266a", "style": "IPY_MODEL_60ef70e25e9d4252a9321c2bcb429189", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c4f826fe2ffa4a63b0302c658c88266a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "60ef70e25e9d4252a9321c2bcb429189": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b0c039f2c184770af205dd08ab2b5ee": {"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_9bd6ba0946544d709a147989d8da5202", "IPY_MODEL_abfec44e75374fe7883ec9f20030e622", "IPY_MODEL_ecb811d8b82741999e9348ec0f40b876", "IPY_MODEL_74d80019dd6a4ad59212cb6f5442ee9d"], "layout": "IPY_MODEL_9b825f6cc49a4384ba89038a6807e8b3"}}, "9bd6ba0946544d709a147989d8da5202": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5411b61734ce46c081be3dab85caa425", "style": "IPY_MODEL_01136da3421140cab2859ee8f2936a1e", "value": "<h4 id=\"DateTrain-TestLeakage(overlap)_EQTUL1P36P6QF5Q14J1RIPZIM-section-6CFNF\"><b>Date Train-Test Leakage (overlap)</b></h4>"}}, "5411b61734ce46c081be3dab85caa425": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01136da3421140cab2859ee8f2936a1e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "abfec44e75374fe7883ec9f20030e622": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3af765a1b58c4e9481ac6000fda8ab25", "style": "IPY_MODEL_e786a159e6394d1fa5d6481416561cd8", "value": "<p>Check test data that is dated earlier than the latest date in train. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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>"}}, "3af765a1b58c4e9481ac6000fda8ab25": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e786a159e6394d1fa5d6481416561cd8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ecb811d8b82741999e9348ec0f40b876": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95fe23a52250462b8eda2aa678770063", "style": "IPY_MODEL_9f9229a238834d708a148b1386447c6b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_48274_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_48274_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_48274_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_48274_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_48274_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_48274_\">\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_48274_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_48274_row0_col1\" class=\"data row0 col1\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_48274_row0_col2\" class=\"data row0 col2\" >Found 100% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "95fe23a52250462b8eda2aa678770063": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f9229a238834d708a148b1386447c6b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "74d80019dd6a4ad59212cb6f5442ee9d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_60e598c3db88410db6950c0ee5062ff1", "IPY_MODEL_e664029d903444f4b2413d86ebd57ba6", "IPY_MODEL_c22f740da40a479b849e0cffe6109ba1"], "layout": "IPY_MODEL_554830be208746e09876769c325d4ec0"}}, "60e598c3db88410db6950c0ee5062ff1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d43bfcb1c39f4a489af9218834503954", "style": "IPY_MODEL_87d9195eff524c898516d483106ed5b4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d43bfcb1c39f4a489af9218834503954": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "87d9195eff524c898516d483106ed5b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e664029d903444f4b2413d86ebd57ba6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_343aaddfe80b476889043aee19d92e46", "style": "IPY_MODEL_83dd27a155864a9dbbe0cccdf2204582", "value": "<div>100% of test data samples are in the date range 2019-10-01 - 2020-01-15, which occurs before last training data date (2020-01-15)</div>"}}, "343aaddfe80b476889043aee19d92e46": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "83dd27a155864a9dbbe0cccdf2204582": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c22f740da40a479b849e0cffe6109ba1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_340e8c8b2541405baa15b44a347296d7", "style": "IPY_MODEL_50a700ef71bd44238a71eb4feaf1bdfd", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-6CFNF\" style=\"font-size: 14px;\">Go to top</a>"}}, "340e8c8b2541405baa15b44a347296d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "50a700ef71bd44238a71eb4feaf1bdfd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "554830be208746e09876769c325d4ec0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b825f6cc49a4384ba89038a6807e8b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "398cdf8bdc0b4b11b0f516a53097cb5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ff7227e385c748919352e00ff5304a71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38c8cbb72c864b5695e90a84e2ce078f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc3b1e512b184493aca343aa5ea7e423": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_205e0dc486cd46c59c450c1f5fcd998e", "IPY_MODEL_9409b87fa92c4f36a5cb57a71355b807"], "layout": "IPY_MODEL_d67c1ded80a3479790edb6f720f7b716"}}, "205e0dc486cd46c59c450c1f5fcd998e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_acdf229df106471f90be175305bd89df", "style": "IPY_MODEL_c919ecbea6ea4299b1d5c167a39c69a6", "value": "<span id=\"summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"></span>"}}, "acdf229df106471f90be175305bd89df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c919ecbea6ea4299b1d5c167a39c69a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9409b87fa92c4f36a5cb57a71355b807": {"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_ab673334958c4183914fe9dc71014a78"], "layout": "IPY_MODEL_bf76b80164e744d69f801dbad3db5624", "selected_index": null}}, "ab673334958c4183914fe9dc71014a78": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1219d85c7e96422d91d3b5b08c19f74a", "IPY_MODEL_dce46c3a08694531b19b4cbac5433e49", "IPY_MODEL_927f7429b5514f88878e220937f12503", "IPY_MODEL_1fac7c4fe4534649bf8b19fd55a3dc2b", "IPY_MODEL_006aff71296948b8ade02e005a073ee8", "IPY_MODEL_1fac7c4fe4534649bf8b19fd55a3dc2b", "IPY_MODEL_3c7c320e38104056bdff52044ce98f03", "IPY_MODEL_1fac7c4fe4534649bf8b19fd55a3dc2b", "IPY_MODEL_6ed76f742bc74d69b92f7900d16774ba", "IPY_MODEL_1fac7c4fe4534649bf8b19fd55a3dc2b", "IPY_MODEL_e079e6deeb0c4a0880c2c91509b5a832", "IPY_MODEL_1fac7c4fe4534649bf8b19fd55a3dc2b", "IPY_MODEL_df700a459a494c8289cb63d2f8c4b063", "IPY_MODEL_1fac7c4fe4534649bf8b19fd55a3dc2b", "IPY_MODEL_0216ab60c23c496b98a1656a3a5a9828"], "layout": "IPY_MODEL_4ec81a98a21b489689051cc441aee0c8"}}, "1219d85c7e96422d91d3b5b08c19f74a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6649db9b3725407fb46f937444d3103c", "style": "IPY_MODEL_eaa7bfde68df471cbb887312b7a87aa5", "value": "<style type=\"text/css\">\n#T_d9971_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9971_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9971_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9971_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d9971_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d9971_\">\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_d9971_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Datasets Size Comparison</a></td>\n      <td id=\"T_d9971_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_d9971_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.32</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row1_col1\" class=\"data row1 col1\" ><a href=\"#CategoryMismatchTrainTest_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Category Mismatch Train Test</a></td>\n      <td id=\"T_d9971_row1_col2\" class=\"data row1 col2\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_d9971_row1_col3\" class=\"data row1 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row2_col1\" class=\"data row2 col1\" ><a href=\"#TrainTestSamplesMix_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Train Test Samples Mix</a></td>\n      <td id=\"T_d9971_row2_col2\" class=\"data row2 col2\" >Percentage of test data samples that appear in train data is less or equal to 10%</td>\n      <td id=\"T_d9971_row2_col3\" class=\"data row2 col3\" >Percent of test data samples that appear in train data: 0.29%</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row3_col1\" class=\"data row3 col1\" ><a href=\"#FeatureLabelCorrelationChange_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Feature Label Correlation Change</a></td>\n      <td id=\"T_d9971_row3_col2\" class=\"data row3 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_d9971_row3_col3\" class=\"data row3 col3\" >Passed for 26 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row4_col1\" class=\"data row4 col1\" ><a href=\"#FeatureLabelCorrelationChange_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Feature Label Correlation Change</a></td>\n      <td id=\"T_d9971_row4_col2\" class=\"data row4 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_d9971_row4_col3\" class=\"data row4 col3\" >Passed for 26 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row5_col1\" class=\"data row5 col1\" ><a href=\"#TrainTestFeatureDrift_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Train Test Feature Drift</a></td>\n      <td id=\"T_d9971_row5_col2\" class=\"data row5 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_d9971_row5_col3\" class=\"data row5 col3\" >Passed for 26 columns out of 26 columns.\nFound column \"numParams\" has the highest numerical drift score: 0.03</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row6_col1\" class=\"data row6 col1\" ><a href=\"#TrainTestLabelDrift_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Train Test Label Drift</a></td>\n      <td id=\"T_d9971_row6_col2\" class=\"data row6 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_d9971_row6_col3\" class=\"data row6 col3\" >Label's drift score Cramer's V is 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row7_col1\" class=\"data row7 col1\" ><a href=\"#MultivariateDrift_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\">Multivariate Drift</a></td>\n      <td id=\"T_d9971_row7_col2\" class=\"data row7 col2\" >Drift value is less than 0.25</td>\n      <td id=\"T_d9971_row7_col3\" class=\"data row7 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_d9971_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row8_col1\" class=\"data row8 col1\" >New Label Train Test</td>\n      <td id=\"T_d9971_row8_col2\" class=\"data row8 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_d9971_row8_col3\" class=\"data row8 col3\" >No new labels found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row9_col1\" class=\"data row9 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_d9971_row9_col2\" class=\"data row9 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_d9971_row9_col3\" class=\"data row9 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d9971_row10_col0\" class=\"data row10 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d9971_row10_col1\" class=\"data row10 col1\" >Date Train-Test Leakage (duplicates)</td>\n      <td id=\"T_d9971_row10_col2\" class=\"data row10 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_d9971_row10_col3\" class=\"data row10 col3\" >No leaked dates found</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6649db9b3725407fb46f937444d3103c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eaa7bfde68df471cbb887312b7a87aa5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dce46c3a08694531b19b4cbac5433e49": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcf02c9016f94cd7932090ef02f59d89", "style": "IPY_MODEL_f5e348083f584c5a9d416bc6fe36154e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "dcf02c9016f94cd7932090ef02f59d89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f5e348083f584c5a9d416bc6fe36154e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "927f7429b5514f88878e220937f12503": {"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_3a759d98bcf749c1bb16a241bf9b3296", "IPY_MODEL_f1c37f2c877943a7acb856b1c54715ea", "IPY_MODEL_278c2e35d6de42c19ba4098b5e2012cf", "IPY_MODEL_001cd57b92e8456bb96e420b77ac6a0a"], "layout": "IPY_MODEL_e142b9255a7d48a181e8ee928c3335b1"}}, "3a759d98bcf749c1bb16a241bf9b3296": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cf09728bcfab48ea8a6bf4ae20109f31", "style": "IPY_MODEL_6479f52f379740e2b5d4ec29f7366ba4", "value": "<h4 id=\"DatasetsSizeComparison_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Datasets Size Comparison</b></h4>"}}, "cf09728bcfab48ea8a6bf4ae20109f31": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6479f52f379740e2b5d4ec29f7366ba4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f1c37f2c877943a7acb856b1c54715ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d5409324aa4445cc9ec6754337105a47", "style": "IPY_MODEL_9b77c11ea803468194a2cccb767d9f5e", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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>"}}, "d5409324aa4445cc9ec6754337105a47": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b77c11ea803468194a2cccb767d9f5e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "278c2e35d6de42c19ba4098b5e2012cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_422f13920a104b048806ec6dd37847bf", "style": "IPY_MODEL_bbe60ed2350649be82abd9613bc132c6", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e3161_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e3161_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e3161_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e3161_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e3161_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e3161_\">\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_e3161_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_e3161_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_e3161_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.32</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "422f13920a104b048806ec6dd37847bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bbe60ed2350649be82abd9613bc132c6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "001cd57b92e8456bb96e420b77ac6a0a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d538c7203c6d4bc8982ccc603f58d584", "IPY_MODEL_7569bf8d37484f7e85a225a090e23123", "IPY_MODEL_bf85647be71143a4bc0ca31513ed012b"], "layout": "IPY_MODEL_5639f75ff08540978881984286f7b665"}}, "d538c7203c6d4bc8982ccc603f58d584": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e61932a55d749a4890ea1392de041ee", "style": "IPY_MODEL_9aa5dc4a98784a5a8e8868271cd3eede", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1e61932a55d749a4890ea1392de041ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9aa5dc4a98784a5a8e8868271cd3eede": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7569bf8d37484f7e85a225a090e23123": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b5f28e0a882a4feea2b33019167cbee2", "style": "IPY_MODEL_c54924ec3a5345a582ad720f7cf3df19", "value": "<style type=\"text/css\">\n#T_3591b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3591b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3591b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3591b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3591b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3591b_\">\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_3591b_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_3591b_row0_col0\" class=\"data row0 col0\" >8626</td>\n      <td id=\"T_3591b_row0_col1\" class=\"data row0 col1\" >2724</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b5f28e0a882a4feea2b33019167cbee2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c54924ec3a5345a582ad720f7cf3df19": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf85647be71143a4bc0ca31513ed012b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c0b49ab79f64710a56930aa633232fa", "style": "IPY_MODEL_e0e84f9bdd36478dad43a15e9504712b", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\" style=\"font-size: 14px;\">Go to top</a>"}}, "1c0b49ab79f64710a56930aa633232fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e0e84f9bdd36478dad43a15e9504712b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5639f75ff08540978881984286f7b665": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e142b9255a7d48a181e8ee928c3335b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1fac7c4fe4534649bf8b19fd55a3dc2b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_54e8fadc90724146a68fe6335996257c", "style": "IPY_MODEL_980a8ab0feb24524b9f104b26313aa18", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "54e8fadc90724146a68fe6335996257c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "980a8ab0feb24524b9f104b26313aa18": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "006aff71296948b8ade02e005a073ee8": {"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_dbc4a98bac764b188ce0b54fd1719de4", "IPY_MODEL_937c6e4d56764130b635daa56366264c", "IPY_MODEL_5c8f56bf5981425bb7dd01ec96f40316", "IPY_MODEL_f99dd652e53f4967b47d377ce220eaaf"], "layout": "IPY_MODEL_fd3914afc2e44106b269b9866a6ac228"}}, "dbc4a98bac764b188ce0b54fd1719de4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f6e6f73bedc745dd85452fb67cb5e613", "style": "IPY_MODEL_01fa876b2fa046dd9966935511d93597", "value": "<h4 id=\"CategoryMismatchTrainTest_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Category Mismatch Train Test</b></h4>"}}, "f6e6f73bedc745dd85452fb67cb5e613": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01fa876b2fa046dd9966935511d93597": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "937c6e4d56764130b635daa56366264c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9eb3e4dead0f417fb929cacb0b9482a8", "style": "IPY_MODEL_644185d8b63b4468b3a6220f3ee2baae", "value": "<p>Find new categories in the test set. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/train_test_validation/plot_category_mismatch_train_test.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9eb3e4dead0f417fb929cacb0b9482a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "644185d8b63b4468b3a6220f3ee2baae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c8f56bf5981425bb7dd01ec96f40316": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c6d81446ba2475f985e43f92cb4b658", "style": "IPY_MODEL_1bd6a35b4eae4002890547b338a36149", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_fa9a9_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa9a9_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa9a9_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa9a9_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa9a9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fa9a9_\">\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_fa9a9_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fa9a9_row0_col1\" class=\"data row0 col1\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_fa9a9_row0_col2\" class=\"data row0 col2\" >No relevant columns to check were found</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1c6d81446ba2475f985e43f92cb4b658": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bd6a35b4eae4002890547b338a36149": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f99dd652e53f4967b47d377ce220eaaf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a6f0f7e523a046cba4dc7ae593d7f4e6", "IPY_MODEL_e37c7b1f95fc410d816a58b7428d3e59", "IPY_MODEL_1f1047384b754193ae36e78c3f18b2e1"], "layout": "IPY_MODEL_cb6078649d3546d08e2d86fe44decf99"}}, "a6f0f7e523a046cba4dc7ae593d7f4e6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_30d212a28ae94bacae044a22e8cba0db", "style": "IPY_MODEL_5b9165c27bff4b3c9a136fbf06f8d110", "value": "<h5><b>Additional Outputs</b></h5>"}}, "30d212a28ae94bacae044a22e8cba0db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5b9165c27bff4b3c9a136fbf06f8d110": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e37c7b1f95fc410d816a58b7428d3e59": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_263fb3411225469a824e7aa0155b9e89", "style": "IPY_MODEL_5d147804d53b4a55abe36a302caa40c9", "value": "<style type=\"text/css\">\n#T_75765_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_75765_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_75765_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_75765_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_75765_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_75765_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Number of new categories</th>\n      <th class=\"col_heading level0 col1\" >Percent of new categories in sample</th>\n      <th class=\"col_heading level0 col2\" >Feature importance</th>\n      <th class=\"col_heading level0 col3\" >New categories examples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Column</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"}}, "263fb3411225469a824e7aa0155b9e89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d147804d53b4a55abe36a302caa40c9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1f1047384b754193ae36e78c3f18b2e1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f567e7fe68c44f2b89d03f5b0414b8c", "style": "IPY_MODEL_de12ea72938546ce984195255f20109c", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\" style=\"font-size: 14px;\">Go to top</a>"}}, "8f567e7fe68c44f2b89d03f5b0414b8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de12ea72938546ce984195255f20109c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb6078649d3546d08e2d86fe44decf99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fd3914afc2e44106b269b9866a6ac228": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c7c320e38104056bdff52044ce98f03": {"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_a5f665fb0e0e4c139c6218eccc807606", "IPY_MODEL_00ea7669dc3448da8b3eb627a2e637a2", "IPY_MODEL_302fd9f4becd47e499abfd1733ba1b22", "IPY_MODEL_90aafbb79235437087fc4bf673727901"], "layout": "IPY_MODEL_6988235a576d48bd96824c6e7d8050ac"}}, "a5f665fb0e0e4c139c6218eccc807606": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_56ab59b2827f4c58b1a9741385d14388", "style": "IPY_MODEL_042ac32b78854023819e891919da4b83", "value": "<h4 id=\"TrainTestSamplesMix_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Train Test Samples Mix</b></h4>"}}, "56ab59b2827f4c58b1a9741385d14388": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "042ac32b78854023819e891919da4b83": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "00ea7669dc3448da8b3eb627a2e637a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ecce6c9b4f5e436faa502dcb84c6ca84", "style": "IPY_MODEL_ecf61281366a4482b8a0553acfeff89d", "value": "<p>Detect samples in the test data that appear also in training data. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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>"}}, "ecce6c9b4f5e436faa502dcb84c6ca84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ecf61281366a4482b8a0553acfeff89d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "302fd9f4becd47e499abfd1733ba1b22": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_086809a4217547ac89f12a6ba311687e", "style": "IPY_MODEL_6cf382939513400abb3a55c6a6700f38", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_943d5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_943d5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_943d5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_943d5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_943d5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_943d5_\">\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_943d5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_943d5_row0_col1\" class=\"data row0 col1\" >Percentage of test data samples that appear in train data is less or equal to 10%</td>\n      <td id=\"T_943d5_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"}}, "086809a4217547ac89f12a6ba311687e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6cf382939513400abb3a55c6a6700f38": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "90aafbb79235437087fc4bf673727901": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_680faba99b5b4a739402318549ad67ff", "IPY_MODEL_8bbfce65adc04a54a92b074c6038a095", "IPY_MODEL_4aab11dc5cea4862af98784e30f08eab", "IPY_MODEL_87604e146c054ff684027e3eebd5b0fb"], "layout": "IPY_MODEL_22e2bfac1a404e2cbe9607d3e1192a60"}}, "680faba99b5b4a739402318549ad67ff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6d9a69e782ed4245ae7deccc195dc8d4", "style": "IPY_MODEL_195b0d0ff966428f99ab6ffbdb120b26", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6d9a69e782ed4245ae7deccc195dc8d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "195b0d0ff966428f99ab6ffbdb120b26": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8bbfce65adc04a54a92b074c6038a095": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_23e83c7920bd4c47b05529516cc36d5d", "style": "IPY_MODEL_fcc2a63d2e5d4503ac380408d46bab04", "value": "<div>0.29% (8 / 2724)                      of test data samples appear in train data</div>"}}, "23e83c7920bd4c47b05529516cc36d5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fcc2a63d2e5d4503ac380408d46bab04": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4aab11dc5cea4862af98784e30f08eab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fff28538d65b4bb69c55feadfd9d33af", "style": "IPY_MODEL_6e5ae6e5e8a0420c8409e3b46f5fefc0", "value": "<style type=\"text/css\">\n#T_76af2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_76af2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_76af2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_76af2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_76af2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_76af2_\">\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_76af2_level0_row0\" class=\"row_heading level0 row0\" >Train indices: 2019-01-02 00:00:00, 2019-02-0.. Tot. 2\nTest indices: 2019-11-20 00:00:00, 2019-11-2.. Tot. 2</th>\n      <td id=\"T_76af2_row0_col0\" class=\"data row0 col0\" >0.85</td>\n      <td id=\"T_76af2_row0_col1\" class=\"data row0 col1\" >-0.43</td>\n      <td id=\"T_76af2_row0_col2\" class=\"data row0 col2\" >2.32</td>\n      <td id=\"T_76af2_row0_col3\" class=\"data row0 col3\" >-0.09</td>\n      <td id=\"T_76af2_row0_col4\" class=\"data row0 col4\" >-0.07</td>\n      <td id=\"T_76af2_row0_col5\" class=\"data row0 col5\" >-1.53</td>\n      <td id=\"T_76af2_row0_col6\" class=\"data row0 col6\" >0.24</td>\n      <td id=\"T_76af2_row0_col7\" class=\"data row0 col7\" >-0.24</td>\n      <td id=\"T_76af2_row0_col8\" class=\"data row0 col8\" >-1.09</td>\n      <td id=\"T_76af2_row0_col9\" class=\"data row0 col9\" >-0.63</td>\n      <td id=\"T_76af2_row0_col10\" class=\"data row0 col10\" >-0.38</td>\n      <td id=\"T_76af2_row0_col11\" class=\"data row0 col11\" >-0.34</td>\n      <td id=\"T_76af2_row0_col12\" class=\"data row0 col12\" >-0.40</td>\n      <td id=\"T_76af2_row0_col13\" class=\"data row0 col13\" >-0.59</td>\n      <td id=\"T_76af2_row0_col14\" class=\"data row0 col14\" >-0.36</td>\n      <td id=\"T_76af2_row0_col15\" class=\"data row0 col15\" >-0.29</td>\n      <td id=\"T_76af2_row0_col16\" class=\"data row0 col16\" >-1.22</td>\n      <td id=\"T_76af2_row0_col17\" class=\"data row0 col17\" >-2.04</td>\n      <td id=\"T_76af2_row0_col18\" class=\"data row0 col18\" >-0.19</td>\n      <td id=\"T_76af2_row0_col19\" class=\"data row0 col19\" >-0.86</td>\n      <td id=\"T_76af2_row0_col20\" class=\"data row0 col20\" >2.30</td>\n      <td id=\"T_76af2_row0_col21\" class=\"data row0 col21\" >-0.40</td>\n      <td id=\"T_76af2_row0_col22\" class=\"data row0 col22\" >-0.04</td>\n      <td id=\"T_76af2_row0_col23\" class=\"data row0 col23\" >-0.28</td>\n      <td id=\"T_76af2_row0_col24\" class=\"data row0 col24\" >-0.43</td>\n      <td id=\"T_76af2_row0_col25\" class=\"data row0 col25\" >-0.23</td>\n      <td id=\"T_76af2_row0_col26\" class=\"data row0 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_76af2_level0_row1\" class=\"row_heading level0 row1\" >Train indices: 2019-01-06 00:00:00\nTest indices: 2019-11-06 00:00:00</th>\n      <td id=\"T_76af2_row1_col0\" class=\"data row1 col0\" >-0.41</td>\n      <td id=\"T_76af2_row1_col1\" class=\"data row1 col1\" >-0.02</td>\n      <td id=\"T_76af2_row1_col2\" class=\"data row1 col2\" >2.98</td>\n      <td id=\"T_76af2_row1_col3\" class=\"data row1 col3\" >-0.09</td>\n      <td id=\"T_76af2_row1_col4\" class=\"data row1 col4\" >-0.07</td>\n      <td id=\"T_76af2_row1_col5\" class=\"data row1 col5\" >-0.42</td>\n      <td id=\"T_76af2_row1_col6\" class=\"data row1 col6\" >0.24</td>\n      <td id=\"T_76af2_row1_col7\" class=\"data row1 col7\" >-0.24</td>\n      <td id=\"T_76af2_row1_col8\" class=\"data row1 col8\" >-1.09</td>\n      <td id=\"T_76af2_row1_col9\" class=\"data row1 col9\" >-0.63</td>\n      <td id=\"T_76af2_row1_col10\" class=\"data row1 col10\" >-0.38</td>\n      <td id=\"T_76af2_row1_col11\" class=\"data row1 col11\" >-0.34</td>\n      <td id=\"T_76af2_row1_col12\" class=\"data row1 col12\" >-0.40</td>\n      <td id=\"T_76af2_row1_col13\" class=\"data row1 col13\" >-0.59</td>\n      <td id=\"T_76af2_row1_col14\" class=\"data row1 col14\" >-0.36</td>\n      <td id=\"T_76af2_row1_col15\" class=\"data row1 col15\" >-0.29</td>\n      <td id=\"T_76af2_row1_col16\" class=\"data row1 col16\" >-1.22</td>\n      <td id=\"T_76af2_row1_col17\" class=\"data row1 col17\" >-2.04</td>\n      <td id=\"T_76af2_row1_col18\" class=\"data row1 col18\" >-0.19</td>\n      <td id=\"T_76af2_row1_col19\" class=\"data row1 col19\" >-0.86</td>\n      <td id=\"T_76af2_row1_col20\" class=\"data row1 col20\" >-0.43</td>\n      <td id=\"T_76af2_row1_col21\" class=\"data row1 col21\" >-0.40</td>\n      <td id=\"T_76af2_row1_col22\" class=\"data row1 col22\" >-0.04</td>\n      <td id=\"T_76af2_row1_col23\" class=\"data row1 col23\" >-0.28</td>\n      <td id=\"T_76af2_row1_col24\" class=\"data row1 col24\" >-0.43</td>\n      <td id=\"T_76af2_row1_col25\" class=\"data row1 col25\" >4.41</td>\n      <td id=\"T_76af2_row1_col26\" class=\"data row1 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_76af2_level0_row2\" class=\"row_heading level0 row2\" >Train indices: 2019-09-24 00:00:00\nTest indices: 2019-10-02 00:00:00</th>\n      <td id=\"T_76af2_row2_col0\" class=\"data row2 col0\" >-0.18</td>\n      <td id=\"T_76af2_row2_col1\" class=\"data row2 col1\" >-0.59</td>\n      <td id=\"T_76af2_row2_col2\" class=\"data row2 col2\" >4.97</td>\n      <td id=\"T_76af2_row2_col3\" class=\"data row2 col3\" >-0.09</td>\n      <td id=\"T_76af2_row2_col4\" class=\"data row2 col4\" >-0.07</td>\n      <td id=\"T_76af2_row2_col5\" class=\"data row2 col5\" >-0.03</td>\n      <td id=\"T_76af2_row2_col6\" class=\"data row2 col6\" >0.24</td>\n      <td id=\"T_76af2_row2_col7\" class=\"data row2 col7\" >-0.24</td>\n      <td id=\"T_76af2_row2_col8\" class=\"data row2 col8\" >-0.71</td>\n      <td id=\"T_76af2_row2_col9\" class=\"data row2 col9\" >-0.52</td>\n      <td id=\"T_76af2_row2_col10\" class=\"data row2 col10\" >-0.38</td>\n      <td id=\"T_76af2_row2_col11\" class=\"data row2 col11\" >-0.31</td>\n      <td id=\"T_76af2_row2_col12\" class=\"data row2 col12\" >-0.40</td>\n      <td id=\"T_76af2_row2_col13\" class=\"data row2 col13\" >-0.59</td>\n      <td id=\"T_76af2_row2_col14\" class=\"data row2 col14\" >-0.36</td>\n      <td id=\"T_76af2_row2_col15\" class=\"data row2 col15\" >-0.29</td>\n      <td id=\"T_76af2_row2_col16\" class=\"data row2 col16\" >-1.22</td>\n      <td id=\"T_76af2_row2_col17\" class=\"data row2 col17\" >-2.04</td>\n      <td id=\"T_76af2_row2_col18\" class=\"data row2 col18\" >-0.19</td>\n      <td id=\"T_76af2_row2_col19\" class=\"data row2 col19\" >-0.86</td>\n      <td id=\"T_76af2_row2_col20\" class=\"data row2 col20\" >-0.43</td>\n      <td id=\"T_76af2_row2_col21\" class=\"data row2 col21\" >-0.40</td>\n      <td id=\"T_76af2_row2_col22\" class=\"data row2 col22\" >-0.04</td>\n      <td id=\"T_76af2_row2_col23\" class=\"data row2 col23\" >-0.28</td>\n      <td id=\"T_76af2_row2_col24\" class=\"data row2 col24\" >-0.43</td>\n      <td id=\"T_76af2_row2_col25\" class=\"data row2 col25\" >4.41</td>\n      <td id=\"T_76af2_row2_col26\" class=\"data row2 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_76af2_level0_row3\" class=\"row_heading level0 row3\" >Train indices: 2019-08-15 00:00:00\nTest indices: 2019-12-02 00:00:00</th>\n      <td id=\"T_76af2_row3_col0\" class=\"data row3 col0\" >-0.09</td>\n      <td id=\"T_76af2_row3_col1\" class=\"data row3 col1\" >-0.54</td>\n      <td id=\"T_76af2_row3_col2\" class=\"data row3 col2\" >4.97</td>\n      <td id=\"T_76af2_row3_col3\" class=\"data row3 col3\" >-0.09</td>\n      <td id=\"T_76af2_row3_col4\" class=\"data row3 col4\" >-0.07</td>\n      <td id=\"T_76af2_row3_col5\" class=\"data row3 col5\" >-0.04</td>\n      <td id=\"T_76af2_row3_col6\" class=\"data row3 col6\" >0.24</td>\n      <td id=\"T_76af2_row3_col7\" class=\"data row3 col7\" >-0.24</td>\n      <td id=\"T_76af2_row3_col8\" class=\"data row3 col8\" >-0.71</td>\n      <td id=\"T_76af2_row3_col9\" class=\"data row3 col9\" >-0.52</td>\n      <td id=\"T_76af2_row3_col10\" class=\"data row3 col10\" >-0.38</td>\n      <td id=\"T_76af2_row3_col11\" class=\"data row3 col11\" >-0.31</td>\n      <td id=\"T_76af2_row3_col12\" class=\"data row3 col12\" >-0.40</td>\n      <td id=\"T_76af2_row3_col13\" class=\"data row3 col13\" >-0.59</td>\n      <td id=\"T_76af2_row3_col14\" class=\"data row3 col14\" >-0.36</td>\n      <td id=\"T_76af2_row3_col15\" class=\"data row3 col15\" >-0.29</td>\n      <td id=\"T_76af2_row3_col16\" class=\"data row3 col16\" >-1.22</td>\n      <td id=\"T_76af2_row3_col17\" class=\"data row3 col17\" >-2.04</td>\n      <td id=\"T_76af2_row3_col18\" class=\"data row3 col18\" >-0.19</td>\n      <td id=\"T_76af2_row3_col19\" class=\"data row3 col19\" >-0.86</td>\n      <td id=\"T_76af2_row3_col20\" class=\"data row3 col20\" >-0.43</td>\n      <td id=\"T_76af2_row3_col21\" class=\"data row3 col21\" >-0.40</td>\n      <td id=\"T_76af2_row3_col22\" class=\"data row3 col22\" >-0.04</td>\n      <td id=\"T_76af2_row3_col23\" class=\"data row3 col23\" >-0.28</td>\n      <td id=\"T_76af2_row3_col24\" class=\"data row3 col24\" >-0.43</td>\n      <td id=\"T_76af2_row3_col25\" class=\"data row3 col25\" >4.41</td>\n      <td id=\"T_76af2_row3_col26\" class=\"data row3 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_76af2_level0_row4\" class=\"row_heading level0 row4\" >Train indices: 2019-08-03 00:00:00, 2019-06-1.. Tot. 4\nTest indices: 2019-12-03 00:00:00</th>\n      <td id=\"T_76af2_row4_col0\" class=\"data row4 col0\" >0.21</td>\n      <td id=\"T_76af2_row4_col1\" class=\"data row4 col1\" >0.70</td>\n      <td id=\"T_76af2_row4_col2\" class=\"data row4 col2\" >2.32</td>\n      <td id=\"T_76af2_row4_col3\" class=\"data row4 col3\" >-0.09</td>\n      <td id=\"T_76af2_row4_col4\" class=\"data row4 col4\" >-0.07</td>\n      <td id=\"T_76af2_row4_col5\" class=\"data row4 col5\" >-1.40</td>\n      <td id=\"T_76af2_row4_col6\" class=\"data row4 col6\" >0.24</td>\n      <td id=\"T_76af2_row4_col7\" class=\"data row4 col7\" >-0.24</td>\n      <td id=\"T_76af2_row4_col8\" class=\"data row4 col8\" >-1.09</td>\n      <td id=\"T_76af2_row4_col9\" class=\"data row4 col9\" >-0.63</td>\n      <td id=\"T_76af2_row4_col10\" class=\"data row4 col10\" >-0.38</td>\n      <td id=\"T_76af2_row4_col11\" class=\"data row4 col11\" >-0.34</td>\n      <td id=\"T_76af2_row4_col12\" class=\"data row4 col12\" >-0.40</td>\n      <td id=\"T_76af2_row4_col13\" class=\"data row4 col13\" >-0.59</td>\n      <td id=\"T_76af2_row4_col14\" class=\"data row4 col14\" >-0.36</td>\n      <td id=\"T_76af2_row4_col15\" class=\"data row4 col15\" >-0.29</td>\n      <td id=\"T_76af2_row4_col16\" class=\"data row4 col16\" >-1.22</td>\n      <td id=\"T_76af2_row4_col17\" class=\"data row4 col17\" >-2.04</td>\n      <td id=\"T_76af2_row4_col18\" class=\"data row4 col18\" >-0.19</td>\n      <td id=\"T_76af2_row4_col19\" class=\"data row4 col19\" >-0.86</td>\n      <td id=\"T_76af2_row4_col20\" class=\"data row4 col20\" >-0.43</td>\n      <td id=\"T_76af2_row4_col21\" class=\"data row4 col21\" >-0.40</td>\n      <td id=\"T_76af2_row4_col22\" class=\"data row4 col22\" >-0.04</td>\n      <td id=\"T_76af2_row4_col23\" class=\"data row4 col23\" >-0.28</td>\n      <td id=\"T_76af2_row4_col24\" class=\"data row4 col24\" >2.34</td>\n      <td id=\"T_76af2_row4_col25\" class=\"data row4 col25\" >-0.23</td>\n      <td id=\"T_76af2_row4_col26\" class=\"data row4 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_76af2_level0_row5\" class=\"row_heading level0 row5\" >Train indices: 2019-04-01 00:00:00\nTest indices: 2019-12-14 00:00:00</th>\n      <td id=\"T_76af2_row5_col0\" class=\"data row5 col0\" >0.21</td>\n      <td id=\"T_76af2_row5_col1\" class=\"data row5 col1\" >0.70</td>\n      <td id=\"T_76af2_row5_col2\" class=\"data row5 col2\" >2.32</td>\n      <td id=\"T_76af2_row5_col3\" class=\"data row5 col3\" >-0.09</td>\n      <td id=\"T_76af2_row5_col4\" class=\"data row5 col4\" >-0.07</td>\n      <td id=\"T_76af2_row5_col5\" class=\"data row5 col5\" >-1.35</td>\n      <td id=\"T_76af2_row5_col6\" class=\"data row5 col6\" >0.24</td>\n      <td id=\"T_76af2_row5_col7\" class=\"data row5 col7\" >-0.24</td>\n      <td id=\"T_76af2_row5_col8\" class=\"data row5 col8\" >-1.09</td>\n      <td id=\"T_76af2_row5_col9\" class=\"data row5 col9\" >-0.63</td>\n      <td id=\"T_76af2_row5_col10\" class=\"data row5 col10\" >-0.38</td>\n      <td id=\"T_76af2_row5_col11\" class=\"data row5 col11\" >-0.34</td>\n      <td id=\"T_76af2_row5_col12\" class=\"data row5 col12\" >-0.40</td>\n      <td id=\"T_76af2_row5_col13\" class=\"data row5 col13\" >-0.59</td>\n      <td id=\"T_76af2_row5_col14\" class=\"data row5 col14\" >-0.36</td>\n      <td id=\"T_76af2_row5_col15\" class=\"data row5 col15\" >-0.29</td>\n      <td id=\"T_76af2_row5_col16\" class=\"data row5 col16\" >-1.22</td>\n      <td id=\"T_76af2_row5_col17\" class=\"data row5 col17\" >-2.04</td>\n      <td id=\"T_76af2_row5_col18\" class=\"data row5 col18\" >-0.19</td>\n      <td id=\"T_76af2_row5_col19\" class=\"data row5 col19\" >-0.86</td>\n      <td id=\"T_76af2_row5_col20\" class=\"data row5 col20\" >-0.43</td>\n      <td id=\"T_76af2_row5_col21\" class=\"data row5 col21\" >-0.40</td>\n      <td id=\"T_76af2_row5_col22\" class=\"data row5 col22\" >-0.04</td>\n      <td id=\"T_76af2_row5_col23\" class=\"data row5 col23\" >-0.28</td>\n      <td id=\"T_76af2_row5_col24\" class=\"data row5 col24\" >2.34</td>\n      <td id=\"T_76af2_row5_col25\" class=\"data row5 col25\" >-0.23</td>\n      <td id=\"T_76af2_row5_col26\" class=\"data row5 col26\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_76af2_level0_row6\" class=\"row_heading level0 row6\" >Train indices: 2019-05-01 00:00:00\nTest indices: 2019-11-26 00:00:00</th>\n      <td id=\"T_76af2_row6_col0\" class=\"data row6 col0\" >1.65</td>\n      <td id=\"T_76af2_row6_col1\" class=\"data row6 col1\" >-0.28</td>\n      <td id=\"T_76af2_row6_col2\" class=\"data row6 col2\" >2.32</td>\n      <td id=\"T_76af2_row6_col3\" class=\"data row6 col3\" >-0.09</td>\n      <td id=\"T_76af2_row6_col4\" class=\"data row6 col4\" >-0.07</td>\n      <td id=\"T_76af2_row6_col5\" class=\"data row6 col5\" >-1.92</td>\n      <td id=\"T_76af2_row6_col6\" class=\"data row6 col6\" >0.24</td>\n      <td id=\"T_76af2_row6_col7\" class=\"data row6 col7\" >-0.24</td>\n      <td id=\"T_76af2_row6_col8\" class=\"data row6 col8\" >-1.09</td>\n      <td id=\"T_76af2_row6_col9\" class=\"data row6 col9\" >-0.63</td>\n      <td id=\"T_76af2_row6_col10\" class=\"data row6 col10\" >-0.38</td>\n      <td id=\"T_76af2_row6_col11\" class=\"data row6 col11\" >-0.34</td>\n      <td id=\"T_76af2_row6_col12\" class=\"data row6 col12\" >-0.40</td>\n      <td id=\"T_76af2_row6_col13\" class=\"data row6 col13\" >-0.59</td>\n      <td id=\"T_76af2_row6_col14\" class=\"data row6 col14\" >-0.36</td>\n      <td id=\"T_76af2_row6_col15\" class=\"data row6 col15\" >-0.29</td>\n      <td id=\"T_76af2_row6_col16\" class=\"data row6 col16\" >-1.22</td>\n      <td id=\"T_76af2_row6_col17\" class=\"data row6 col17\" >-2.04</td>\n      <td id=\"T_76af2_row6_col18\" class=\"data row6 col18\" >-0.19</td>\n      <td id=\"T_76af2_row6_col19\" class=\"data row6 col19\" >-0.86</td>\n      <td id=\"T_76af2_row6_col20\" class=\"data row6 col20\" >-0.43</td>\n      <td id=\"T_76af2_row6_col21\" class=\"data row6 col21\" >-0.40</td>\n      <td id=\"T_76af2_row6_col22\" class=\"data row6 col22\" >-0.04</td>\n      <td id=\"T_76af2_row6_col23\" class=\"data row6 col23\" >-0.28</td>\n      <td id=\"T_76af2_row6_col24\" class=\"data row6 col24\" >2.34</td>\n      <td id=\"T_76af2_row6_col25\" class=\"data row6 col25\" >-0.23</td>\n      <td id=\"T_76af2_row6_col26\" class=\"data row6 col26\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "fff28538d65b4bb69c55feadfd9d33af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e5ae6e5e8a0420c8409e3b46f5fefc0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "87604e146c054ff684027e3eebd5b0fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b97d40c1b4944d9bb8b0354722f720e0", "style": "IPY_MODEL_2050988d5ff94f72b14710f950d36b96", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\" style=\"font-size: 14px;\">Go to top</a>"}}, "b97d40c1b4944d9bb8b0354722f720e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2050988d5ff94f72b14710f950d36b96": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22e2bfac1a404e2cbe9607d3e1192a60": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6988235a576d48bd96824c6e7d8050ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ed76f742bc74d69b92f7900d16774ba": {"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_60167cdfd5524789b14a5e60f861c9fe", "IPY_MODEL_e9a1510f3cd74141907ffb962e568d12", "IPY_MODEL_976e800a5a264e3ab45505cf10b3bebc", "IPY_MODEL_5a59ee5c5f07411e8e1579b8fb1c68fa"], "layout": "IPY_MODEL_6deafc23f4a54e03b10bf7ca819a767c"}}, "60167cdfd5524789b14a5e60f861c9fe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_185758294d354d029097131dff7f776f", "style": "IPY_MODEL_d160b98ada36452aa7b6c8b4c97ab01c", "value": "<h4 id=\"FeatureLabelCorrelationChange_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Feature Label Correlation Change</b></h4>"}}, "185758294d354d029097131dff7f776f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d160b98ada36452aa7b6c8b4c97ab01c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9a1510f3cd74141907ffb962e568d12": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b62f3d56af854c7ab65d71ad4d8ba75d", "style": "IPY_MODEL_f9ef93fc888a4994bed78cb000f79de7", "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/0.9/checks_gallery/tabular/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>"}}, "b62f3d56af854c7ab65d71ad4d8ba75d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9ef93fc888a4994bed78cb000f79de7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "976e800a5a264e3ab45505cf10b3bebc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b7eb7d29f082464bb5901f772ba7ec66", "style": "IPY_MODEL_001ca24273d9412a92e69eeca2677cb7", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d2f4c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f4c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f4c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f4c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f4c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d2f4c_\">\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_d2f4c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d2f4c_row0_col1\" class=\"data row0 col1\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_d2f4c_row0_col2\" class=\"data row0 col2\" >Passed for 26 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d2f4c_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d2f4c_row1_col1\" class=\"data row1 col1\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_d2f4c_row1_col2\" class=\"data row1 col2\" >Passed for 26 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b7eb7d29f082464bb5901f772ba7ec66": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "001ca24273d9412a92e69eeca2677cb7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5a59ee5c5f07411e8e1579b8fb1c68fa": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2622387dc3a14379858142d4489a32ea", "IPY_MODEL_11a1d0eb2ae64627bc8c3f81e9c1897b", "IPY_MODEL_155038efc3be4bd183ae1ceb7b7c8b19", "IPY_MODEL_ed20764e79634ee08be12b29c704dbff", "IPY_MODEL_a0b654fcc9d34a5ebd22ae0a65a3f605", "IPY_MODEL_52bfadd879d6433d8528f938fb8cc2cc", "IPY_MODEL_a06be2306eff448dbcb022e265ca207f", "IPY_MODEL_db5a15ac237f4f2d8d69dfecc0f39bc2", "IPY_MODEL_c35f14a975a04db385240d7a7866c9e4", "IPY_MODEL_d3d97067ae8e410eb1cbd3437c4a3f17", "IPY_MODEL_730ed9cb78d74f8f87b511c3ed853761", "IPY_MODEL_59cc93c1ddfe45cbae1d5bc8c79a9a6d", "IPY_MODEL_22ef94d057cb4c5882dea26d5e1fc47a"], "layout": "IPY_MODEL_a54e59c6d22249a0bac714669fbf528a"}}, "2622387dc3a14379858142d4489a32ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ac89c293ac344fff8bcea9bbac81bb22", "style": "IPY_MODEL_46f3b6630b124453a52ca102846b75b2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ac89c293ac344fff8bcea9bbac81bb22": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "46f3b6630b124453a52ca102846b75b2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "11a1d0eb2ae64627bc8c3f81e9c1897b": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.21</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["urlLength", "numDigits", "ext_other", "ext_net", "ext_info"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "953d0caf-6739-4b55-9a43-b8e8fadf7268"}, {"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>0.26</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["urlLength", "numDigits", "ext_other", "ext_net", "ext_info"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "36961de5-159c-43a1-9b55-aff84c563134"}], "_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"}], 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"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": "NH2nOLjSyj8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "ULvrgiG10D8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="}]}, "155038efc3be4bd183ae1ceb7b7c8b19": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a516a52325354eaa8028c63002bb2cb3", "style": "IPY_MODEL_80a862c3922c42b8aecf5561cd73ba75", "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/en/stable/checks_gallery/tabular/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "a516a52325354eaa8028c63002bb2cb3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80a862c3922c42b8aecf5561cd73ba75": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed20764e79634ee08be12b29c704dbff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d3130d08e6e14e66b01133456c68da07", "style": "IPY_MODEL_4b477aa37c844fc8856d6de6c409199a", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "d3130d08e6e14e66b01133456c68da07": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b477aa37c844fc8856d6de6c409199a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a0b654fcc9d34a5ebd22ae0a65a3f605": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b536277bb004d849eeb1dba40c0d9fc", "style": "IPY_MODEL_33945b78942543859b68311182851fcf", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "1b536277bb004d849eeb1dba40c0d9fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33945b78942543859b68311182851fcf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "52bfadd879d6433d8528f938fb8cc2cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a6cdd35c97534db69eda4df435d2cef9", "style": "IPY_MODEL_a2945c2a27594a13bb5b61fa6283ff5b", "value": "<div>Can indicate that this feature's success in predicting the label is actually due to data leakage, </div>"}}, "a6cdd35c97534db69eda4df435d2cef9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2945c2a27594a13bb5b61fa6283ff5b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a06be2306eff448dbcb022e265ca207f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3679b71f48ee485a8cda04c293235e51", "style": "IPY_MODEL_d23825dffe9c4637bfd0885a3b86a7e7", "value": "<div>   meaning that the feature holds information that is based on the label to begin with.</div>"}}, "3679b71f48ee485a8cda04c293235e51": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d23825dffe9c4637bfd0885a3b86a7e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db5a15ac237f4f2d8d69dfecc0f39bc2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5b8a68c148214fc4920ae97c420d0f39", "style": "IPY_MODEL_cfaa9ae6b0df4a1784020cdf9ad9bfad", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "5b8a68c148214fc4920ae97c420d0f39": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cfaa9ae6b0df4a1784020cdf9ad9bfad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c35f14a975a04db385240d7a7866c9e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f3b50f22ff14a82ad99d0014ae88c32", "style": "IPY_MODEL_c7749557de134c1e8924b39dedb1f730", "value": "<div>   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test </div>"}}, "5f3b50f22ff14a82ad99d0014ae88c32": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7749557de134c1e8924b39dedb1f730": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d3d97067ae8e410eb1cbd3437c4a3f17": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c09145dbe1d3445a901dfb9bcf28bea9", "style": "IPY_MODEL_bb61edf4c01b41c8bd14676964efcd92", "value": "<div>   can be explained by leakage in train that is not relevant to a new dataset.</div>"}}, "c09145dbe1d3445a901dfb9bcf28bea9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bb61edf4c01b41c8bd14676964efcd92": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "730ed9cb78d74f8f87b511c3ed853761": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f5a99b9fa29a4831b6adcb15cf7e6af5", "style": "IPY_MODEL_5f23a7b70b5e4da4a15d397a706a0557", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "f5a99b9fa29a4831b6adcb15cf7e6af5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f23a7b70b5e4da4a15d397a706a0557": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59cc93c1ddfe45cbae1d5bc8c79a9a6d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_94d75074a6b4461c94393c4f37ddb1df", "style": "IPY_MODEL_9c02c4a51bbb4e859286b8a7730c4214", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "94d75074a6b4461c94393c4f37ddb1df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c02c4a51bbb4e859286b8a7730c4214": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22ef94d057cb4c5882dea26d5e1fc47a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4484f386f212435db640f86db48d7af3", "style": "IPY_MODEL_7533429acfbd48298fd5bd7f39849448", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\" style=\"font-size: 14px;\">Go to top</a>"}}, "4484f386f212435db640f86db48d7af3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7533429acfbd48298fd5bd7f39849448": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a54e59c6d22249a0bac714669fbf528a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6deafc23f4a54e03b10bf7ca819a767c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e079e6deeb0c4a0880c2c91509b5a832": {"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_58dd4d1250464c699e60705808af2f34", "IPY_MODEL_fbc2e63ff848479d8b8368f557915f87", "IPY_MODEL_e2fd7da1803a4429a02703e588b49df4", "IPY_MODEL_0b0e58f2e8cc492cafe588289c4e28a0"], "layout": "IPY_MODEL_efe56af0bd6848dc802ea8917805c392"}}, "58dd4d1250464c699e60705808af2f34": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c9a9149f9ef4d9792253e4287ace75f", "style": "IPY_MODEL_39cbdbd0adfb48fe90d35491e3565e71", "value": "<h4 id=\"TrainTestFeatureDrift_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Train Test Feature Drift</b></h4>"}}, "2c9a9149f9ef4d9792253e4287ace75f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39cbdbd0adfb48fe90d35491e3565e71": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbc2e63ff848479d8b8368f557915f87": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_da927b1bd56a4a88ba252bffcb912605", "style": "IPY_MODEL_a7574e33dba44bd1b061027fc7e88d43", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/train_test_validation/plot_train_test_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "da927b1bd56a4a88ba252bffcb912605": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a7574e33dba44bd1b061027fc7e88d43": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2fd7da1803a4429a02703e588b49df4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5ed3b661f616481ea2f01272b50ea7c0", "style": "IPY_MODEL_bc472bbf83a141fe80019866e2c8fff6", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_fc3b9_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc3b9_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc3b9_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc3b9_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc3b9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fc3b9_\">\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_fc3b9_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fc3b9_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_fc3b9_row0_col2\" class=\"data row0 col2\" >Passed for 26 columns out of 26 columns.\nFound column \"numParams\" has the highest numerical drift score: 0.03</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5ed3b661f616481ea2f01272b50ea7c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc472bbf83a141fe80019866e2c8fff6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b0e58f2e8cc492cafe588289c4e28a0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ee975f47ee724a24afc82445053e30f0", "IPY_MODEL_3ce773db464747668d977954fc922ed5", "IPY_MODEL_8a9783997f3843eaab1cf82a7eddcb9e", "IPY_MODEL_8cb0ac9109bc4800a7c137ad39f81a4f", "IPY_MODEL_1107bed751d340d8a306a0d86a830f31", "IPY_MODEL_b357c7c3d05d4323813cf2fa9f179728", "IPY_MODEL_28c8379457964d4a83b19b4ae3fb662c", "IPY_MODEL_747fa1336627475486a6fd9e567c54a2", "IPY_MODEL_7d423b717dd94562bec1fa6cdff36dfb", "IPY_MODEL_28e4dd85990d45e685daa0c62f89787c"], "layout": "IPY_MODEL_faa15692cfaa43ab98c5673b209e6ff9"}}, "ee975f47ee724a24afc82445053e30f0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fc92515fc73c4be1803bff9aabd986f5", "style": "IPY_MODEL_383ac8e1f515434289b49140ff68e147", "value": "<h5><b>Additional Outputs</b></h5>"}}, "fc92515fc73c4be1803bff9aabd986f5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "383ac8e1f515434289b49140ff68e147": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3ce773db464747668d977954fc922ed5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4dfca4b45e9c463fbba03b5ed5687cae", "style": "IPY_MODEL_304b9daf535942709208513b7b04e4ce", "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 features, sorted\n                by the sum of the drift score and the feature importance and showing only the top 5 features, according to the sum of the drift score and the feature importance.\n            </span></div>"}}, "4dfca4b45e9c463fbba03b5ed5687cae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "304b9daf535942709208513b7b04e4ce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8a9783997f3843eaab1cf82a7eddcb9e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0fc6334018474ee5a12f3855d7424467", "style": "IPY_MODEL_49bcfc0ccd85497f97dfbfbd672f9e34", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "0fc6334018474ee5a12f3855d7424467": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "49bcfc0ccd85497f97dfbfbd672f9e34": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8cb0ac9109bc4800a7c137ad39f81a4f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c36ae14d30f4013be18ec5a67d04689", "style": "IPY_MODEL_48f707271f3649409ed5b6ba5139efc1", "value": "<div>If available, the plot titles also show the feature importance (FI) rank</div>"}}, "5c36ae14d30f4013be18ec5a67d04689": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "48f707271f3649409ed5b6ba5139efc1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1107bed751d340d8a306a0d86a830f31": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.025332892851435362], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "c8d1b311-d7d1-4223-b9c2-49b478544095"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": {"dtype": "float64", "shape": [12]}, "xaxis": "x2", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y2", "type": "bar", "uid": "2a2a03f6-4758-4a7a-b415-95021e20b240"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": {"dtype": "float64", "shape": [12]}, "xaxis": "x2", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y2", "type": "bar", "uid": "9d4af835-9c22-427e-8167-3acf5049b4fe"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": 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"28e4dd85990d45e685daa0c62f89787c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd3599a9aac94ac3a349d147f4b040fc", "style": "IPY_MODEL_a6fc725ac0994268993f90bedf08e092", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\" style=\"font-size: 14px;\">Go to top</a>"}}, "fd3599a9aac94ac3a349d147f4b040fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a6fc725ac0994268993f90bedf08e092": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "faa15692cfaa43ab98c5673b209e6ff9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "efe56af0bd6848dc802ea8917805c392": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df700a459a494c8289cb63d2f8c4b063": {"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_7e521b490c0c423da3b01a95bd7f2eb2", "IPY_MODEL_60f173e78ff1481186e975b31af8e3da", "IPY_MODEL_68187755de7b4958a1564d3b0b243833", "IPY_MODEL_594c6d71386a4581b8c2ea60e209a4fb"], "layout": "IPY_MODEL_9f0c3f5218a045e780c21a4166c2b8a1"}}, "7e521b490c0c423da3b01a95bd7f2eb2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e69a86b2bbc430fba1b2c4199a4586f", "style": "IPY_MODEL_592f3ade610f45e6bd0b66ea5f4c7a97", "value": "<h4 id=\"TrainTestLabelDrift_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Train Test Label Drift</b></h4>"}}, "4e69a86b2bbc430fba1b2c4199a4586f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "592f3ade610f45e6bd0b66ea5f4c7a97": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "60f173e78ff1481186e975b31af8e3da": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bd2032ff1acf4125925bc399af01a55d", "style": "IPY_MODEL_e67be601e086497ebc0567603d377164", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "bd2032ff1acf4125925bc399af01a55d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e67be601e086497ebc0567603d377164": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "68187755de7b4958a1564d3b0b243833": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aff31c8c9dda4c4799a9987f4470d7cd", "style": "IPY_MODEL_a32d6bdfb9d94850909e88b0eccfb5a3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_fefd8_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fefd8_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fefd8_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fefd8_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fefd8_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fefd8_\">\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_fefd8_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fefd8_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_fefd8_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "aff31c8c9dda4c4799a9987f4470d7cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a32d6bdfb9d94850909e88b0eccfb5a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "594c6d71386a4581b8c2ea60e209a4fb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4984769cdb794a0789637835b21d8805", "IPY_MODEL_0d97814a50e149a48bcb5cd74b9c08a4", "IPY_MODEL_29f0eb3983d940ddb4aa8364188385b8", "IPY_MODEL_42746e68ec3d4c889da27b057b56c806", "IPY_MODEL_922bc981d44c400abf887e6cad019fc1"], "layout": "IPY_MODEL_2f981f38d83a463aafa382d9637a2a1f"}}, "4984769cdb794a0789637835b21d8805": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2221c54e07274835a8e07ff7cbe77cff", "style": "IPY_MODEL_8b72e4ff7e1e4854825a3a2b82cf405c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "2221c54e07274835a8e07ff7cbe77cff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b72e4ff7e1e4854825a3a2b82cf405c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d97814a50e149a48bcb5cd74b9c08a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_18c656e208124be2adb44e37d04fe786", "style": "IPY_MODEL_e93eb7f454b949638a63035f390890b4", "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 label.\n            </span></div>"}}, "18c656e208124be2adb44e37d04fe786": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e93eb7f454b949638a63035f390890b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29f0eb3983d940ddb4aa8364188385b8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2968a7b7cf824d71b164fd569af3cd9b", "style": "IPY_MODEL_d81ce6d5578247aea08110014cc9e921", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "2968a7b7cf824d71b164fd569af3cd9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d81ce6d5578247aea08110014cc9e921": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42746e68ec3d4c889da27b057b56c806": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", 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"linecolor": "black", "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=="}]}, "922bc981d44c400abf887e6cad019fc1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_387920dc051648689a43954098855678", "style": "IPY_MODEL_b48ec9a221194a62b85f0147be4583e6", "value": "<br><a href=\"#summary_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\" style=\"font-size: 14px;\">Go to top</a>"}}, "387920dc051648689a43954098855678": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b48ec9a221194a62b85f0147be4583e6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2f981f38d83a463aafa382d9637a2a1f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f0c3f5218a045e780c21a4166c2b8a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0216ab60c23c496b98a1656a3a5a9828": {"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_0b40bc82598f4ee8be72c6e3dd94aee7", "IPY_MODEL_56d7ac53c5124ff3b5afda9a6cf531ec", "IPY_MODEL_6552d06a776447b6a72c21f4a11df394", "IPY_MODEL_ffe1eebe3bfd4f8eb18127b3eff7fc76"], "layout": "IPY_MODEL_e94d5ce76ce54b67a6d9f0532306323b"}}, "0b40bc82598f4ee8be72c6e3dd94aee7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ddcfcc33c4db4062ad2c01adfaf767b7", "style": "IPY_MODEL_ed8579f81bb4483eb61b604eca0c03a4", "value": "<h4 id=\"MultivariateDrift_EQTUL1P36P6QF5Q14J1RIPZIM-section-HOLQC\"><b>Multivariate Drift</b></h4>"}}, "ddcfcc33c4db4062ad2c01adfaf767b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed8579f81bb4483eb61b604eca0c03a4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "56d7ac53c5124ff3b5afda9a6cf531ec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_007c7d3ac7ae4425890383031d91d680", "style": "IPY_MODEL_9a946f2c8a4e410bbbc6c336bc1554fa", "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/0.9/checks_gallery/tabular/train_test_validation/plot_multivariate_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "007c7d3ac7ae4425890383031d91d680": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a946f2c8a4e410bbbc6c336bc1554fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6552d06a776447b6a72c21f4a11df394": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_75163e536fc54ab88a653c81c207df81", "style": "IPY_MODEL_cdd1f06c147f43a48659593eb6b235af", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_25260_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25260_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25260_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25260_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25260_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_25260_\">\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_25260_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_25260_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.25</td>\n      <td id=\"T_25260_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"}}, "75163e536fc54ab88a653c81c207df81": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cdd1f06c147f43a48659593eb6b235af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ffe1eebe3bfd4f8eb18127b3eff7fc76": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5901ac744e20420e8555954a97b26086", "IPY_MODEL_01128b1b5e624165ae2b9eb813bdbb20", "IPY_MODEL_1fe425eb0ffa4b439063a766141c9ef4", "IPY_MODEL_46f124bd593c4fe3976ff84c34d36161", "IPY_MODEL_c33470436ef44ef8b06bf987cc45a2c4", "IPY_MODEL_247f1a6530024171aee78117b47d04cd", "IPY_MODEL_49017abd612d4848850353efa2d01308", "IPY_MODEL_b2ec17302d7341aab291122ee3087be4", "IPY_MODEL_7da5008125e44c2ea357896eef02a51c", "IPY_MODEL_9e561acf1a6c42ceaf38a9ca75e4548a", "IPY_MODEL_71992af1405845c3adda993395ec1f0c"], "layout": "IPY_MODEL_4068141d5aed41f885e0a5b0a357635e"}}, "5901ac744e20420e8555954a97b26086": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_212a0e24ce1f4610aa42a295adee7984", "style": "IPY_MODEL_1661c64081af43ada94a0f98b57862c4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "212a0e24ce1f4610aa42a295adee7984": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1661c64081af43ada94a0f98b57862c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "01128b1b5e624165ae2b9eb813bdbb20": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f2cc40226a84ba896c4cd64973a8643", "style": "IPY_MODEL_674ab25ef07b4b25a4bad7d19d506a91", "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>"}}, "3f2cc40226a84ba896c4cd64973a8643": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "674ab25ef07b4b25a4bad7d19d506a91": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1fe425eb0ffa4b439063a766141c9ef4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3b6ff040565a48ada9be423686e50182", "style": "IPY_MODEL_e6c692f56c4d4a2f85733be4ca02996f", "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>"}}, "3b6ff040565a48ada9be423686e50182": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e6c692f56c4d4a2f85733be4ca02996f": {"model_name": "DescriptionStyleModel", "model_module": 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col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a7b1c_row0_col0\" class=\"data row0 col0\" >Index Train Test Leakage</td>\n      <td id=\"T_a7b1c_row0_col1\" class=\"data row0 col1\" >DatasetValidationError: Dataset does not contain an index. see <a href=\"https://docs.deepchecks.com/0.9/user-guide/tabular/dataset_object.html?html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Dataset docs</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ea1ea19c40534bf5b24299760004e0cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b94ae545f35f4f7997e28aeff4d370f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "11c772a25f62407d8a5b6f71c31baaf6": {"model_name": "LayoutModel", "model_module": 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    </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)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
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      <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>
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          <th>bscr</th>
          <th>sscr</th>
          <th>ext_com</th>
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          <th>2019-01-01</th>
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          <td>-0.125958</td>
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          <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>
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          <th>2019-01-01</th>
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          <td>-0.414405</td>
          <td>-0.174310</td>
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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>
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          <th>entropy</th>
          <th>hasHttp</th>
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          <th>bscr</th>
          <th>sscr</th>
          <th>ext_com</th>
          <th>ext_country</th>
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          <th>2019-10-01</th>
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    <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=[])








.. 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:
    |###        | 3/11 [Time: 00:00, Check=Confusion Matrix Report]
    Model Evaluation Suite:
    |#####      | 5/11 [Time: 00:00, Check=Simple Model Comparison]One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]

    Model Evaluation Suite:
    |#####      | 5/11 [Time: 00:18, Check=Weak Segments Performance]One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]

    Model Evaluation Suite:
    |######     | 6/11 [Time: 00:40, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |#########  | 9/11 [Time: 00:40, Check=Unused Features]          
                                                                               

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Comparison</b></h4>"}}, "26d6cdc8db4044d19af4b28a69450fc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36299270e85342bab07824c8490dccbe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88e54e373bf74d21a0bdced7aac6354a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13f16bab23384e9d83126453bca02bc6", "style": "IPY_MODEL_3e764807e88f4982879e797244f2cb3e", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "13f16bab23384e9d83126453bca02bc6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e764807e88f4982879e797244f2cb3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7a191f87972476197d294e0233008f2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aa253c503c074346b587bec6cc4727a4", "style": "IPY_MODEL_f0022113d84842c994c56547eeefb54a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_30835_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_30835_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_30835_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_30835_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_30835_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_30835_\">\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_30835_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_30835_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_30835_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"}}, "aa253c503c074346b587bec6cc4727a4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f0022113d84842c994c56547eeefb54a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cc75e46ecbe4413ab094ec6e4b1b69e2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_da02c6daad524168b4c75182e3fe26a9", "IPY_MODEL_cb7712c86ea44dcd88be4e39a046de74", "IPY_MODEL_dd76e692195c469d8bf64ac762aef43e"], "layout": "IPY_MODEL_e4a48023d90c4fb7b93e09581f308b60"}}, "da02c6daad524168b4c75182e3fe26a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41adae0198e5472da54912e435e808cb", "style": "IPY_MODEL_b8c4cb7428b04ba7a99bf7f97bf49253", "value": "<h5><b>Additional Outputs</b></h5>"}}, "41adae0198e5472da54912e435e808cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b8c4cb7428b04ba7a99bf7f97bf49253": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb7712c86ea44dcd88be4e39a046de74": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=LogisticRegression model<br>Metric=F1<br>value=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "LogisticRegression model", "marker": {"color": "#636efa", "pattern": {"shape": ""}}, "name": "LogisticRegression model", "offsetgroup": "LogisticRegression model", "orientation": "v", "showlegend": true, "x": [0, 1], "xaxis": "x", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y", "type": "histogram", "uid": "087243a8-0d89-4a76-8d44-d6da3d68004e"}, {"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": "c5660cfa-1c31-449b-a5b7-dcda540bb313"}], "_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}}, 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"IPY_MODEL_bbb8ba0e1a174fc48dfc7177169240e5", "selected_index": null}}, "7e4d9ec3fbf34d88b9f69825bc268276": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e3db671060fb4a7b9f95fac2e03099b8", "IPY_MODEL_0d5250c62a0348f7bab8b99ca53b144e", "IPY_MODEL_9a2ab9a45512495c865e95b9590cee0b", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_0b666b0f718441ec8094bd8edccc844a", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_df303ff947b0475fbf0ed09fd426cafa", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_c79fcc59fe8b43b795b75d379e4d6c95", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_254b6779ef3b43e6b3f47124bda0c4c3", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_6e3cac2b8d054179b757fe05098d2c71", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_326ecc8267434437a4a22c0ebfb57363", "IPY_MODEL_91cf99c12d9449668628c7476995f30c", "IPY_MODEL_1d931de87265426683adacf6e43cab9e"], "layout": "IPY_MODEL_eed64eddeda643ddaefa59e6e7b96cb6"}}, "e3db671060fb4a7b9f95fac2e03099b8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_943a54775602487bb5fee50b27a6365d", "style": "IPY_MODEL_a580df5646754e64b1eaf32dfd60e041", "value": "<style type=\"text/css\">\n#T_fae0d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fae0d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fae0d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fae0d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fae0d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fae0d_\">\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_fae0d_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestPerformance_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">Train Test Performance</a></td>\n      <td id=\"T_fae0d_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_fae0d_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_fae0d_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row1_col1\" class=\"data row1 col1\" ><a href=\"#ROCReport-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">ROC Report - Train Dataset</a></td>\n      <td id=\"T_fae0d_row1_col2\" class=\"data row1 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_fae0d_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_fae0d_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row2_col1\" class=\"data row2 col1\" ><a href=\"#ROCReport-TestDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">ROC Report - Test Dataset</a></td>\n      <td id=\"T_fae0d_row2_col2\" class=\"data row2 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_fae0d_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_fae0d_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row3_col1\" class=\"data row3 col1\" ><a href=\"#TrainTestPredictionDrift_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">Train Test Prediction Drift</a></td>\n      <td id=\"T_fae0d_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_fae0d_row3_col3\" class=\"data row3 col3\" >Found model prediction Earth Mover's Distance drift score of 4.91E-3</td>\n    </tr>\n    <tr>\n      <td id=\"T_fae0d_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row4_col1\" class=\"data row4 col1\" ><a href=\"#UnusedFeatures-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">Unused Features - Train Dataset</a></td>\n      <td id=\"T_fae0d_row4_col2\" class=\"data row4 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_fae0d_row4_col3\" class=\"data row4 col3\" >Found 2 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_fae0d_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row5_col1\" class=\"data row5 col1\" ><a href=\"#UnusedFeatures-TestDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">Unused Features - Test Dataset</a></td>\n      <td id=\"T_fae0d_row5_col2\" class=\"data row5 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_fae0d_row5_col3\" class=\"data row5 col3\" >Found 1 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_fae0d_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_fae0d_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_fae0d_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 6.28e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_fae0d_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fae0d_row7_col1\" class=\"data row7 col1\" ><a href=\"#ModelInferenceTime-TestDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_fae0d_row7_col2\" class=\"data row7 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_fae0d_row7_col3\" class=\"data row7 col3\" >Found average inference time (seconds): 3.39e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "943a54775602487bb5fee50b27a6365d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a580df5646754e64b1eaf32dfd60e041": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d5250c62a0348f7bab8b99ca53b144e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c286d487a9b40309d597000e8fc420d", "style": "IPY_MODEL_d766dfef42b644938d2fd848974d594a", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "0c286d487a9b40309d597000e8fc420d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d766dfef42b644938d2fd848974d594a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a2ab9a45512495c865e95b9590cee0b": {"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_3deb3a4781d5461591716b80f1c388bc", "IPY_MODEL_0e59a9d31095481a90b095907b62ee9b", "IPY_MODEL_0ba8ccaecb284aef9664bca2b713340c", "IPY_MODEL_41d9060b022441c59099a41de896182e"], "layout": "IPY_MODEL_3dab165275c34311aa9cde06b20bc312"}}, "3deb3a4781d5461591716b80f1c388bc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c84acb060c1345899dc15b1b9667a3f8", "style": "IPY_MODEL_509b0636e1534863af27f9ed60b76cff", "value": "<h4 id=\"TrainTestPerformance_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>Train Test Performance</b></h4>"}}, "c84acb060c1345899dc15b1b9667a3f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "509b0636e1534863af27f9ed60b76cff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0e59a9d31095481a90b095907b62ee9b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c52a71d7bf744cc2a183ce2f86e50b60", "style": "IPY_MODEL_bda19325183b4d6aac4e2658f281de3e", "value": "<p>Summarize given model performance on the train and test datasets based on selected scorers. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c52a71d7bf744cc2a183ce2f86e50b60": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bda19325183b4d6aac4e2658f281de3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ba8ccaecb284aef9664bca2b713340c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e5b1b918b9b44ec6995f06cb9fb144f8", "style": "IPY_MODEL_7e1336e345c949ddb2048080c4829d72", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_482bd_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_482bd_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_482bd_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_482bd_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_482bd_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_482bd_\">\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_482bd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_482bd_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_482bd_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"}}, "e5b1b918b9b44ec6995f06cb9fb144f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e1336e345c949ddb2048080c4829d72": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "41d9060b022441c59099a41de896182e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0891176874224d0e95de9f2910077d43", "IPY_MODEL_558d3c96fe464b70b47c183696391a02", "IPY_MODEL_b5feb7180c7d46baa25393dc6fc383d8"], "layout": "IPY_MODEL_29cf3964e1c64e239577062517bca7be"}}, "0891176874224d0e95de9f2910077d43": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_176e856b66934f73a1f5055064a56f20", "style": "IPY_MODEL_90b4dca84b5b474a80365b0917908a50", "value": "<h5><b>Additional Outputs</b></h5>"}}, "176e856b66934f73a1f5055064a56f20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90b4dca84b5b474a80365b0917908a50": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "558d3c96fe464b70b47c183696391a02": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "c186a3fc-540c-4801-8362-6b240f5f59c9"}, {"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": "ef76e003-f3d2-4890-9a69-88fa2ab8223c"}, {"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": "8f47cc3d-3490-493a-a5de-f728cabf83b8"}, {"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": "f1d579ed-356e-466b-9b91-6e99a667bda6"}, {"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": "01bf2b0c-17bd-4fc1-9774-e8f697e99bca"}, {"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": "8576b907-62e2-41aa-86bd-1bfd03ff9163"}], "_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"}], "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": 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href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "a9725afd91514b78a3739f21cfe4e8ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6483a4f68eb46b79718fa5475d4de4b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29cf3964e1c64e239577062517bca7be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3dab165275c34311aa9cde06b20bc312": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "91cf99c12d9449668628c7476995f30c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f0db9051b7ae41d9b5c91ac308e5d469", "style": 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"IPY_MODEL_7824d276171549969f1ae7708002ad13", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6aa31cdbc43e4cb0905ce3a81db92325": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7824d276171549969f1ae7708002ad13": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96ebc6cd21dd4d30b72ea042ecc87c69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fc7a6e9202c54228a04377f7574578bc", "style": "IPY_MODEL_40428a401fb048aa8bef5436ae009cdc", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a2d02_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d02_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d02_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d02_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2d02_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a2d02_\">\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_a2d02_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a2d02_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_a2d02_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"}}, "fc7a6e9202c54228a04377f7574578bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "40428a401fb048aa8bef5436ae009cdc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0eb16b2112d441c79050d0b3294e63bd": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2c5af4af1efd440eb26b8ef69ba68b1b", "IPY_MODEL_9a7e3a14665e4512aea21f6e02ba6383", "IPY_MODEL_69754f576b4d4598917cb9cb4055a34e", "IPY_MODEL_28d0fa1117c843bb849d4ae6536991e5"], "layout": "IPY_MODEL_0d2e447f566d44878ef04c54d6e3fef1"}}, "2c5af4af1efd440eb26b8ef69ba68b1b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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"69754f576b4d4598917cb9cb4055a34e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b46788402fb47b29c20cdb153e547bb", "style": "IPY_MODEL_2c51ac72533d4bbbb811f60a3c1a9821", "value": "<div><span style=\"font-size:0.8em\"><i>\n            The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined\n            as sensitivity + specificity - 1\n            </i></span></div>"}}, "2b46788402fb47b29c20cdb153e547bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c51ac72533d4bbbb811f60a3c1a9821": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "28d0fa1117c843bb849d4ae6536991e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_17e590ac6d85413fad6ba2fe006e3469", "style": "IPY_MODEL_093b6dad3d5647e48636e96bafa1cab8", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "17e590ac6d85413fad6ba2fe006e3469": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "093b6dad3d5647e48636e96bafa1cab8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d2e447f566d44878ef04c54d6e3fef1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b2c2775f05e468f8fc012c15f296fb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df303ff947b0475fbf0ed09fd426cafa": {"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_1fa6da8192dd4a4dbd764009a77bd469", "IPY_MODEL_19fd3696755d4c38bdda1ef360419acd", "IPY_MODEL_5716ef7c232d47b4b765506f732b5bcc", "IPY_MODEL_9de436edb95e42fca61e3744bcaa7dc1"], "layout": "IPY_MODEL_66eea18621fc42459c56494cfac93cba"}}, "1fa6da8192dd4a4dbd764009a77bd469": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3358de28db6c4e3fbdb39af976d3d653", "style": "IPY_MODEL_933c0a3beddc4f6895a6caf92f79d2e1", "value": "<h4 id=\"ROCReport-TestDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>ROC Report - Test Dataset</b></h4>"}}, "3358de28db6c4e3fbdb39af976d3d653": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "933c0a3beddc4f6895a6caf92f79d2e1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "19fd3696755d4c38bdda1ef360419acd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_986f368f912845d49b8db347950c74b2", "style": "IPY_MODEL_e93abc21864c4eb998374766b74836c3", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "986f368f912845d49b8db347950c74b2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e93abc21864c4eb998374766b74836c3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5716ef7c232d47b4b765506f732b5bcc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bdfc2867c3cb4299b029925db876fdd6", "style": "IPY_MODEL_9a4782b313dd4bf19a70e120e20523a7", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_90c65_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90c65_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90c65_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90c65_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90c65_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_90c65_\">\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_90c65_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_90c65_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_90c65_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"}}, "bdfc2867c3cb4299b029925db876fdd6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a4782b313dd4bf19a70e120e20523a7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9de436edb95e42fca61e3744bcaa7dc1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c166dde0efb0479192f3858d941e95a4", "IPY_MODEL_0e190486adc24eaebc5eeef2db58e98a", "IPY_MODEL_9a879bdd828545d185bfc57dcd142298", "IPY_MODEL_33483ce153aa487489846fc0ce672671"], "layout": "IPY_MODEL_eb7643b5b24c45138c5a862f1b720b63"}}, "c166dde0efb0479192f3858d941e95a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ae9d175f4fa3456aacee4cf3afdf2ded", "style": "IPY_MODEL_309a28b5279a485782fb00e974fb2f6b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ae9d175f4fa3456aacee4cf3afdf2ded": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "309a28b5279a485782fb00e974fb2f6b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0e190486adc24eaebc5eeef2db58e98a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "auc = 0.89", "x": {"dtype": "float64", "shape": [118]}, "y": {"dtype": "float64", "shape": [118]}, "type": "scatter", "uid": "9ba19cf8-707f-49d1-96f9-42bd17801480"}, {"hovertemplate": "TPR: %{y:.2%}<br>FPR: %{x:.2%}<br>Youden's Index: 0.953", "marker": {"size": 15}, "mode": "markers", "showlegend": false, "x": [0.20238095238095238], "y": [0.8174242424242424], "type": "scatter", "uid": "d742d3ab-1512-4477-8700-782f2aff738e"}, {"line": {"color": "#444", "dash": "dash", "width": 2}, "showlegend": false, "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "14766347-149f-4050-a7af-d29a97c98d46"}], "_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", 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"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 for binary data"}, "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": 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"9a879bdd828545d185bfc57dcd142298": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_778f87bfa314462b8f925fb53b178771", "style": "IPY_MODEL_2086b77ecce54ff6ab3401d9d388f46a", "value": "<div><span style=\"font-size:0.8em\"><i>\n            The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined\n            as sensitivity + specificity - 1\n            </i></span></div>"}}, "778f87bfa314462b8f925fb53b178771": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2086b77ecce54ff6ab3401d9d388f46a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "33483ce153aa487489846fc0ce672671": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8145d19a5bb4e3b8dcb012cd90b67bc", "style": "IPY_MODEL_73f3a2e37ec5439f89df939fbf40ff37", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "d8145d19a5bb4e3b8dcb012cd90b67bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73f3a2e37ec5439f89df939fbf40ff37": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eb7643b5b24c45138c5a862f1b720b63": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "66eea18621fc42459c56494cfac93cba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c79fcc59fe8b43b795b75d379e4d6c95": {"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_383f74b1b3cb491181c373e989fe3d06", "IPY_MODEL_59b75f3e7b464f1a9ca380fd8a0a1e01", "IPY_MODEL_723e00901a8b4172a01b1c1479b9ec36", "IPY_MODEL_6a6005797ce44c69a7f50ad291929ab1"], "layout": "IPY_MODEL_7b1a7321f1264ea0b7ddfa957c032691"}}, "383f74b1b3cb491181c373e989fe3d06": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5b4ecbf725f44c68ab729c85cf91fc33", "style": "IPY_MODEL_aaf1190707e244099c13437c66336b78", "value": "<h4 id=\"TrainTestPredictionDrift_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>Train Test Prediction Drift</b></h4>"}}, "5b4ecbf725f44c68ab729c85cf91fc33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aaf1190707e244099c13437c66336b78": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59b75f3e7b464f1a9ca380fd8a0a1e01": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8d579f8076d4ef490e39ca461bfbeec", "style": "IPY_MODEL_c25c177e4eb74ec19f1b4aeb46ab19aa", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d8d579f8076d4ef490e39ca461bfbeec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c25c177e4eb74ec19f1b4aeb46ab19aa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "723e00901a8b4172a01b1c1479b9ec36": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e727efa90f9345b78b35c38a8a80c42d", "style": "IPY_MODEL_f69a732ed2434296bbc71caf8373c0d5", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_be6d2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be6d2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be6d2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be6d2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_be6d2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_be6d2_\">\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_be6d2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_be6d2_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_be6d2_row0_col2\" class=\"data row0 col2\" >Found model prediction Earth Mover's Distance drift score of 4.91E-3</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e727efa90f9345b78b35c38a8a80c42d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f69a732ed2434296bbc71caf8373c0d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6a6005797ce44c69a7f50ad291929ab1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a195e44073e7486183cc2635f2a83fdc", "IPY_MODEL_fbef41fddc2a45709e2da5445b667620", "IPY_MODEL_5a7c07d084514fdc99c65d89667ea411", "IPY_MODEL_02f35b92e9ff4af8833b250d4219c06a", "IPY_MODEL_13d5fbb2061e4ce4a183cfc5fb5e0d48"], "layout": "IPY_MODEL_a7294f5fd5964ef482cc24a3d98fd872"}}, "a195e44073e7486183cc2635f2a83fdc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c943e7f8f2d74c76936c0a9ec1e33280", "style": "IPY_MODEL_ecbf6304ee744845943422cd5cc00abf", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c943e7f8f2d74c76936c0a9ec1e33280": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ecbf6304ee744845943422cd5cc00abf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbef41fddc2a45709e2da5445b667620": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d202355ba3214822868418dd4516ef00", "style": "IPY_MODEL_a8b747f6d26648789db1b98a8712c0c4", "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>"}}, "d202355ba3214822868418dd4516ef00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8b747f6d26648789db1b98a8712c0c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5a7c07d084514fdc99c65d89667ea411": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e7b2cd339e9b4521980752b5f3d13ae0", "style": "IPY_MODEL_0db3b52e675b442aacd2a82d941d2e52", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "e7b2cd339e9b4521980752b5f3d13ae0": {"model_name": "LayoutModel", 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"13d5fbb2061e4ce4a183cfc5fb5e0d48": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_18a7a801f2384ab496c4b34bffdca210", "style": "IPY_MODEL_71b8594cfbc34503ad66da065e779092", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "18a7a801f2384ab496c4b34bffdca210": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71b8594cfbc34503ad66da065e779092": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7294f5fd5964ef482cc24a3d98fd872": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7b1a7321f1264ea0b7ddfa957c032691": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "254b6779ef3b43e6b3f47124bda0c4c3": {"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_eba2e409404741ccb7cafe347e3c3179", "IPY_MODEL_44700de4f33341cda2e87c504f428d1a", "IPY_MODEL_105b2bcfc3294694831ca310fa643af2", "IPY_MODEL_acde91854a104611b75bc7accd136382"], "layout": "IPY_MODEL_a9abe732382c41819206156f02de66fc"}}, "eba2e409404741ccb7cafe347e3c3179": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_739f9e2af1044bd59be6d7621cbfc174", "style": "IPY_MODEL_13e770b6024146978654811194820dc3", "value": "<h4 id=\"UnusedFeatures-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>Unused Features - Train Dataset</b></h4>"}}, "739f9e2af1044bd59be6d7621cbfc174": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13e770b6024146978654811194820dc3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "44700de4f33341cda2e87c504f428d1a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_991c7b7b369f47708896ebd98d6109bb", "style": "IPY_MODEL_646451ac7af641a294ea9c09a5c64249", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "991c7b7b369f47708896ebd98d6109bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "646451ac7af641a294ea9c09a5c64249": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "105b2bcfc3294694831ca310fa643af2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f18991beccea4963a9061b29283d81f7", "style": "IPY_MODEL_d3b85c84f0fa4da390de00edd0273d6d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3a2b6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3a2b6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3a2b6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3a2b6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3a2b6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3a2b6_\">\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_3a2b6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3a2b6_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_3a2b6_row0_col2\" class=\"data row0 col2\" >Found 2 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f18991beccea4963a9061b29283d81f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3b85c84f0fa4da390de00edd0273d6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "acde91854a104611b75bc7accd136382": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_67f79f18479842048c7b0c71168f8e6b", "IPY_MODEL_1c31eb5704e14cb5b5471c7b69beff7e", "IPY_MODEL_8827141a6d264d0d830c668b37e9cc78", "IPY_MODEL_b6bfcea96ad64a09918c7cc7bb91a42b"], "layout": "IPY_MODEL_710a328e856d485d87cb0eb736762619"}}, "67f79f18479842048c7b0c71168f8e6b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_658a28ddf3574cb69f023536ff43f3d8", "style": "IPY_MODEL_aee0c47c16aa4af0890ba668c58f21ed", "value": "<h5><b>Additional Outputs</b></h5>"}}, "658a28ddf3574cb69f023536ff43f3d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aee0c47c16aa4af0890ba668c58f21ed": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1c31eb5704e14cb5b5471c7b69beff7e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c32c0f8158df4f509ad969dae3bf8e1b", "style": "IPY_MODEL_5255503f1f27492a9b39f60637059c2e", "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>"}}, "c32c0f8158df4f509ad969dae3bf8e1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5255503f1f27492a9b39f60637059c2e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8827141a6d264d0d830c668b37e9cc78": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "aedd0639-d16d-4471-b43b-a37341c7cc7f"}, {"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": "13e8d8d8-f04a-45e9-81d6-516681753026"}], "_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": "aHZgxWTLE0Ak+cUNBu0IQPPE3ZCA0hBAKcROOFEqEEA7PV4/t1oSQPhfWAB8Ag1A8Rbq87NdDEA="}]}, "b6bfcea96ad64a09918c7cc7bb91a42b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ab64127867f647b4947f991b6e719c7e", "style": "IPY_MODEL_102de18a668441dbb12a7f0b7d93c665", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "ab64127867f647b4947f991b6e719c7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "102de18a668441dbb12a7f0b7d93c665": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "710a328e856d485d87cb0eb736762619": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a9abe732382c41819206156f02de66fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e3cac2b8d054179b757fe05098d2c71": {"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_4b858ddb79e346f7a03c721b47c8283a", "IPY_MODEL_5ce4100693df478da3b613a5ecd0f084", "IPY_MODEL_b7629eef419a421f88a217b01fce15af", "IPY_MODEL_c3c0a4e0d4a94b0f86c2bb014c6f7864"], "layout": "IPY_MODEL_e8772982f4274872937e25b6e2e7ed95"}}, "4b858ddb79e346f7a03c721b47c8283a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a7ac239aae2a48eca301b56d87f6dbc8", "style": "IPY_MODEL_4c2d8c4c056d4d9c8ed6d489813838de", "value": "<h4 id=\"UnusedFeatures-TestDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>Unused Features - Test Dataset</b></h4>"}}, "a7ac239aae2a48eca301b56d87f6dbc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c2d8c4c056d4d9c8ed6d489813838de": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5ce4100693df478da3b613a5ecd0f084": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cce2dc47bfcb44d7984d7fc4dd58032d", "style": "IPY_MODEL_1a0bb8e4313f4fa78108ebcfdbae601f", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cce2dc47bfcb44d7984d7fc4dd58032d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1a0bb8e4313f4fa78108ebcfdbae601f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7629eef419a421f88a217b01fce15af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3747c797b07c43478e2f87f3bbfefc77", "style": "IPY_MODEL_d872160eb19749279f1b46d767e8f3b3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5e306_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e306_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e306_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e306_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e306_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5e306_\">\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_5e306_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5e306_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_5e306_row0_col2\" class=\"data row0 col2\" >Found 1 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3747c797b07c43478e2f87f3bbfefc77": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d872160eb19749279f1b46d767e8f3b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c3c0a4e0d4a94b0f86c2bb014c6f7864": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cf029778be29426bbfb08c793cbfa07a", "IPY_MODEL_2d47fec173774b0ea9c89324a99d57b3", "IPY_MODEL_c1526c12c1714790924d541945b911c4", "IPY_MODEL_24d0415db50c48338d40cb99440146e5"], "layout": "IPY_MODEL_f7142070ee3b440692c94be805f25806"}}, "cf029778be29426bbfb08c793cbfa07a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6acc34eedc724026ad54df9b2d974bc8", "style": "IPY_MODEL_e582a8e7ea9a44e2a06e8cf96f85a866", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6acc34eedc724026ad54df9b2d974bc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e582a8e7ea9a44e2a06e8cf96f85a866": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2d47fec173774b0ea9c89324a99d57b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f0b2a1122dc4f10aa9abc77d3f5669b", "style": "IPY_MODEL_72dbbff2c68449b08a67c5d4dcc5a330", "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>"}}, "8f0b2a1122dc4f10aa9abc77d3f5669b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72dbbff2c68449b08a67c5d4dcc5a330": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c1526c12c1714790924d541945b911c4": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "89be08ca-dbb9-4567-b74e-443b4a2e0b9a"}, {"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": "8736df5c-5a95-44c5-a7ba-f63167aa8a4f"}], "_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"}, 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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": "kgMw5voSEkBmszhrU7QPQIOCFR6OlxRACvElyMv8CUB6zb1R6T0JQFP/Oco52hJA"}]}, "24d0415db50c48338d40cb99440146e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90d63c55502a4656b6ade14eaab940e8", "style": "IPY_MODEL_0447380513db43d3a0c50cf546b68159", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "90d63c55502a4656b6ade14eaab940e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0447380513db43d3a0c50cf546b68159": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f7142070ee3b440692c94be805f25806": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e8772982f4274872937e25b6e2e7ed95": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "326ecc8267434437a4a22c0ebfb57363": {"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_f5c87becbeac4ece861552f1c8203f3c", "IPY_MODEL_d717bfa4e4134c508124a9cfabcd68a9", "IPY_MODEL_3589952b2bfe4862b7c2a31c385f8507", "IPY_MODEL_2b0054e2f3e243e1b68d446dab1dc0dc"], "layout": "IPY_MODEL_ef1855e5d1cc4d3e9777df58b5e9df17"}}, "f5c87becbeac4ece861552f1c8203f3c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10b82a6b8a8f4084a1e0fbff3cdbb149", "style": "IPY_MODEL_982a1b5f52b44c96be1892afd976be75", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "10b82a6b8a8f4084a1e0fbff3cdbb149": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "982a1b5f52b44c96be1892afd976be75": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d717bfa4e4134c508124a9cfabcd68a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e8c1d24014c4327a93052e78f71e634", "style": "IPY_MODEL_6fc78836a4d04be19fdc05c6c7ad2081", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4e8c1d24014c4327a93052e78f71e634": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6fc78836a4d04be19fdc05c6c7ad2081": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3589952b2bfe4862b7c2a31c385f8507": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f74c840c437345f688d7407b9fe6c9cb", "style": "IPY_MODEL_a0d03f554f2845d1b8e0303a39841176", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_93328_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_93328_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_93328_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_93328_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_93328_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_93328_\">\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_93328_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_93328_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_93328_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 6.28e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f74c840c437345f688d7407b9fe6c9cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a0d03f554f2845d1b8e0303a39841176": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2b0054e2f3e243e1b68d446dab1dc0dc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9ea68f07c0da4944907b2f53b6c5b814", "IPY_MODEL_f97502a3f15b45cdae9bdcc27d891176", "IPY_MODEL_e0334948c5af405091f1fa91a3553387", "IPY_MODEL_3c06b440f38c48e2bf32d9366ce695d3"], "layout": "IPY_MODEL_7395448b1bba4625b8eeb28dbd07489e"}}, "9ea68f07c0da4944907b2f53b6c5b814": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_755db19ab2c04835a47d9a8d90dc53ab", "style": "IPY_MODEL_117a63c5733c4a7aa3238d2db05e8f29", "value": "<h5><b>Additional Outputs</b></h5>"}}, "755db19ab2c04835a47d9a8d90dc53ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "117a63c5733c4a7aa3238d2db05e8f29": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f97502a3f15b45cdae9bdcc27d891176": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8edb22ef4ee545439cda040c8f9d1a88", "style": "IPY_MODEL_3ae5e711c688400c98d0747bdaef9f98", "value": "<div>Average model inference time for one sample (in seconds): 6.28e-06</div>"}}, "8edb22ef4ee545439cda040c8f9d1a88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ae5e711c688400c98d0747bdaef9f98": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0334948c5af405091f1fa91a3553387": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e2a957e6dfe4c3fb4d530265c45498a", "style": "IPY_MODEL_8f6f595c55db4cde81b9aef327528dcf", "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>"}}, "1e2a957e6dfe4c3fb4d530265c45498a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f6f595c55db4cde81b9aef327528dcf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c06b440f38c48e2bf32d9366ce695d3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_27f9af84bbc94cf39c60c5ab2a6efd1a", "style": "IPY_MODEL_7091654612a24ecead78f1505e020397", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "27f9af84bbc94cf39c60c5ab2a6efd1a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7091654612a24ecead78f1505e020397": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7395448b1bba4625b8eeb28dbd07489e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef1855e5d1cc4d3e9777df58b5e9df17": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1d931de87265426683adacf6e43cab9e": {"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_e46caec4a18b4693a6bef6b80232ad7b", "IPY_MODEL_5a8558c6436b4be58da11abe018a102c", "IPY_MODEL_0b9b05cd63f942fcaa41c0f5bed99a5d", "IPY_MODEL_8e7bacd569d84a73b364ef8475eccec6"], "layout": "IPY_MODEL_9510efcb4e65405e9e9293fe95240a2e"}}, "e46caec4a18b4693a6bef6b80232ad7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c4aedeb2f7c4ad0bb2af97bca1aa473", "style": "IPY_MODEL_1aab5139700e4c099d518c38b2bb8a82", "value": "<h4 id=\"ModelInferenceTime-TestDataset_3O18A5UR7KO64T026RJRNY204-section-XDP5G\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "7c4aedeb2f7c4ad0bb2af97bca1aa473": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1aab5139700e4c099d518c38b2bb8a82": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5a8558c6436b4be58da11abe018a102c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7d3e81d712234cfaa70b630e6a510023", "style": "IPY_MODEL_64bd9a031f4140e88f1acb858152a123", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7d3e81d712234cfaa70b630e6a510023": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64bd9a031f4140e88f1acb858152a123": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b9b05cd63f942fcaa41c0f5bed99a5d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4d9da0415c64470e81cbb28b2220c411", "style": "IPY_MODEL_14d3aef0faad4ee98754ef5e4da0df2d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a237b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a237b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a237b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a237b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a237b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a237b_\">\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_a237b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a237b_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_a237b_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 3.39e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4d9da0415c64470e81cbb28b2220c411": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14d3aef0faad4ee98754ef5e4da0df2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e7bacd569d84a73b364ef8475eccec6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_728d53f3c56b4e84b8dc2dc28634f9b2", "IPY_MODEL_6e160d998c7c43edb68329682425cf82", "IPY_MODEL_0e2143135f334ce0849241a071302545", "IPY_MODEL_a998a3a0825546f4bd18610bbba5faed"], "layout": "IPY_MODEL_e1074041897749cba7f2bcb0cf7369ca"}}, "728d53f3c56b4e84b8dc2dc28634f9b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90af46d3220447d688b63d5c9abf0440", "style": "IPY_MODEL_03c5b200b4574e87916f178387364c52", "value": "<h5><b>Additional Outputs</b></h5>"}}, "90af46d3220447d688b63d5c9abf0440": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03c5b200b4574e87916f178387364c52": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6e160d998c7c43edb68329682425cf82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_becce4dc4c5d4254a2a35d47b8862fa6", "style": "IPY_MODEL_0790baedf96f4324befef621b23d1e72", "value": "<div>Average model inference time for one sample (in seconds): 3.39e-06</div>"}}, "becce4dc4c5d4254a2a35d47b8862fa6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0790baedf96f4324befef621b23d1e72": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0e2143135f334ce0849241a071302545": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_35b19856579d475a8d99f7909836f511", "style": "IPY_MODEL_b4dddbc077ff44bcb543d551209e790d", "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>"}}, "35b19856579d475a8d99f7909836f511": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4dddbc077ff44bcb543d551209e790d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a998a3a0825546f4bd18610bbba5faed": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67632cc44b0242e6ab43faf04c53fdc3", "style": "IPY_MODEL_b19e4d66726049d4b875ee38050d4856", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-XDP5G\" style=\"font-size: 14px;\">Go to top</a>"}}, "67632cc44b0242e6ab43faf04c53fdc3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b19e4d66726049d4b875ee38050d4856": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e1074041897749cba7f2bcb0cf7369ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9510efcb4e65405e9e9293fe95240a2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eed64eddeda643ddaefa59e6e7b96cb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bbb8ba0e1a174fc48dfc7177169240e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e4fa375d7694d45932bf6b627a91643": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d977d526f1042df98e187b447efc44d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7039ff31eb1a4a639aee2f19da1c1ecd", "IPY_MODEL_33de082a45854de798c60de3f09163e3"], "layout": "IPY_MODEL_163791b6e0b94c8db4968adfc4c07574"}}, "7039ff31eb1a4a639aee2f19da1c1ecd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_278380088a7f494f8fc948aef5bb66ff", "style": "IPY_MODEL_1e3cf664834e47688fd3a6b33563b34b", "value": "<span id=\"summary_3O18A5UR7KO64T026RJRNY204-section-JLQM5\"></span>"}}, "278380088a7f494f8fc948aef5bb66ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1e3cf664834e47688fd3a6b33563b34b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "33de082a45854de798c60de3f09163e3": {"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_8b74e3419c864dd28f276d9b0abc132d"], "layout": "IPY_MODEL_29b8785dea5d49a0a8659a0351a3acd7", "selected_index": null}}, "8b74e3419c864dd28f276d9b0abc132d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ef014cb7a04f47868ddfcf878e0d58e7", "IPY_MODEL_5845cd4540a447e4aae102c73fd7cc96", "IPY_MODEL_3771c36590364439b779df83aec54e32", "IPY_MODEL_d51b94377cf345458322d4435b8e1332", "IPY_MODEL_69e7934822fb4d0cabc874e1fd89d248", "IPY_MODEL_d51b94377cf345458322d4435b8e1332", "IPY_MODEL_aa04817613204c11a4527aa5ff2a4445", "IPY_MODEL_d51b94377cf345458322d4435b8e1332", "IPY_MODEL_dd80102419e3479ca4705f654e1ae4a3"], "layout": "IPY_MODEL_f53f3da8a57f402a932302283253bcdc"}}, "ef014cb7a04f47868ddfcf878e0d58e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_63bed0c0dc0f4adf9c8a0a0520c45cd4", "style": "IPY_MODEL_d367e7bf88394ae19b97146ef121ac63", "value": "<style type=\"text/css\">\n#T_2d4f0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2d4f0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2d4f0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2d4f0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2d4f0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2d4f0_\">\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_2d4f0_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrixReport-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\">Confusion Matrix Report - Train Dataset</a></td>\n      <td id=\"T_2d4f0_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_2d4f0_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrixReport-TestDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\">Confusion Matrix Report - Test Dataset</a></td>\n      <td id=\"T_2d4f0_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_2d4f0_row2_col0\" class=\"data row2 col0\" ><a href=\"#CalibrationMetric-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\">Calibration Metric - Train Dataset</a></td>\n      <td id=\"T_2d4f0_row2_col1\" class=\"data row2 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_2d4f0_row3_col0\" class=\"data row3 col0\" ><a href=\"#CalibrationMetric-TestDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\">Calibration Metric - Test Dataset</a></td>\n      <td id=\"T_2d4f0_row3_col1\" class=\"data row3 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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"}}, "63bed0c0dc0f4adf9c8a0a0520c45cd4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d367e7bf88394ae19b97146ef121ac63": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5845cd4540a447e4aae102c73fd7cc96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8fbe5b93bf594f7dbbf11c7cf9f2d7ef", "style": "IPY_MODEL_4cb22fa6862148d6bd44c28a3a4ef58f", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8fbe5b93bf594f7dbbf11c7cf9f2d7ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4cb22fa6862148d6bd44c28a3a4ef58f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3771c36590364439b779df83aec54e32": {"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_19bee42a5aa845eb8a03ff5d2ef8af89", "IPY_MODEL_00ea912824384cc88fabf79929bdf414", "IPY_MODEL_8b849e20c57347e4b54020af069d5c02"], "layout": "IPY_MODEL_144dc6c61bb14d0d9d4c57919f301a94"}}, "19bee42a5aa845eb8a03ff5d2ef8af89": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_66480d970fa347c68f2ef2b1047a3926", "style": "IPY_MODEL_e9569a1042aa48d3903a172026b56b5c", "value": "<h4 id=\"ConfusionMatrixReport-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\"><b>Confusion Matrix Report - Train Dataset</b></h4>"}}, "66480d970fa347c68f2ef2b1047a3926": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e9569a1042aa48d3903a172026b56b5c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "00ea912824384cc88fabf79929bdf414": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7fff496e130c4917a0a85a5cbd916640", "style": "IPY_MODEL_f4a0dd49ebab4a149b8a678c2848ca5b", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7fff496e130c4917a0a85a5cbd916640": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f4a0dd49ebab4a149b8a678c2848ca5b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8b849e20c57347e4b54020af069d5c02": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1f7b691aab1045d6b01c922726bb4d9f", "IPY_MODEL_0e575544c7b24568a84fe4632500c138", "IPY_MODEL_4fdcd090f34c4cb79af90f9e51fc5def"], "layout": "IPY_MODEL_57a6b102d2604939adcfa6bdb3abe86a"}}, "1f7b691aab1045d6b01c922726bb4d9f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_89141271d7f946babd0a90bcf50664a5", "style": "IPY_MODEL_a89cc4abc09c44eb840b0da5d7b3ba38", "value": "<h5><b>Additional Outputs</b></h5>"}}, "89141271d7f946babd0a90bcf50664a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a89cc4abc09c44eb840b0da5d7b3ba38": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0e575544c7b24568a84fe4632500c138": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[8360, 0], [265, 1]], "texttemplate": "%{z}%<br>(%{text})", "x": [0, 1], "y": [0, 1], "z": [["100", "0"], ["99.62", "0.38"]], "type": "heatmap", "uid": "38b44b9a-c1a0-40b9-acea-4fe4fad846b5"}], "_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": "Percent Out of True Values (Count)"}, "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}}, "4fdcd090f34c4cb79af90f9e51fc5def": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b5d9f68cc8541a289d618d40b515561", "style": "IPY_MODEL_a1d940e0f7144f7996f7dbae194f13a1", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-JLQM5\" style=\"font-size: 14px;\">Go to top</a>"}}, "6b5d9f68cc8541a289d618d40b515561": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1d940e0f7144f7996f7dbae194f13a1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57a6b102d2604939adcfa6bdb3abe86a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "144dc6c61bb14d0d9d4c57919f301a94": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d51b94377cf345458322d4435b8e1332": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8fcd93f2f4d8453fafe8939378bf8cb3", "style": "IPY_MODEL_dc6803658333442b8e3d390ef6f73673", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8fcd93f2f4d8453fafe8939378bf8cb3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc6803658333442b8e3d390ef6f73673": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "69e7934822fb4d0cabc874e1fd89d248": {"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_ba84b9e6250041608b5110797846ce56", "IPY_MODEL_83d8216c9346453684e06cf59e588e5c", "IPY_MODEL_07b225c75e1945c7b5157ceb536be84b"], "layout": "IPY_MODEL_405c0fe90a6844169cf9a1ca4c8c8870"}}, "ba84b9e6250041608b5110797846ce56": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_237adb6365b945e48e540fbd7b6e41ce", "style": "IPY_MODEL_7426e5339b5b4e86bed8cdb3ed5e80ac", "value": "<h4 id=\"ConfusionMatrixReport-TestDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\"><b>Confusion Matrix Report - Test Dataset</b></h4>"}}, "237adb6365b945e48e540fbd7b6e41ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7426e5339b5b4e86bed8cdb3ed5e80ac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "83d8216c9346453684e06cf59e588e5c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5af9a9f9007342ddb736bc6686b13317", "style": "IPY_MODEL_0deec6a43a3846c48d10be43291e4a32", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5af9a9f9007342ddb736bc6686b13317": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0deec6a43a3846c48d10be43291e4a32": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "07b225c75e1945c7b5157ceb536be84b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bf27204212fb422b823df49682a03334", "IPY_MODEL_f56a537fc0e34729a260f10dddb5aead", "IPY_MODEL_a8f36062af7641ffb49f774af36709e4"], "layout": "IPY_MODEL_2bd446047223455eb4a8b786eb66d7a1"}}, "bf27204212fb422b823df49682a03334": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2d820432d09c4c39babee0e5c1608820", "style": "IPY_MODEL_c79a01c440d54cc9b56b58933b153c24", "value": "<h5><b>Additional Outputs</b></h5>"}}, 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{"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"], 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"_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "a8f36062af7641ffb49f774af36709e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0eddcbebcf2e44259b397eb7ac51dd0a", "style": "IPY_MODEL_358486e569ab4ea48b0a06b9c397482d", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-JLQM5\" style=\"font-size: 14px;\">Go to top</a>"}}, "0eddcbebcf2e44259b397eb7ac51dd0a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "358486e569ab4ea48b0a06b9c397482d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2bd446047223455eb4a8b786eb66d7a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "405c0fe90a6844169cf9a1ca4c8c8870": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa04817613204c11a4527aa5ff2a4445": {"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_daa7c55ffed6431d8d146f5eecb9e5bc", "IPY_MODEL_bb1e17e71bff414fb891bd591f265986", "IPY_MODEL_5bc8492ff18f42ac9ea7c60b04b9265b"], "layout": "IPY_MODEL_363a3d875b544b32bba756a81a88e547"}}, "daa7c55ffed6431d8d146f5eecb9e5bc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_687e58553c4747b88f9dba6b01335deb", "style": "IPY_MODEL_ab5f66740f4844958ccc40bee2463ccb", "value": "<h4 id=\"CalibrationMetric-TrainDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\"><b>Calibration Metric - Train Dataset</b></h4>"}}, "687e58553c4747b88f9dba6b01335deb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab5f66740f4844958ccc40bee2463ccb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb1e17e71bff414fb891bd591f265986": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21f15cd1001b438781c0319203141c27", "style": "IPY_MODEL_154f3227926347e9b15c4cc605457895", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "21f15cd1001b438781c0319203141c27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "154f3227926347e9b15c4cc605457895": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5bc8492ff18f42ac9ea7c60b04b9265b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c1b6571507854bce8a4bf420fd83e322", "IPY_MODEL_dfaf2cbd86ac4564a334ddcc2be030f3", "IPY_MODEL_de6a6e6aec80425f930795e8e5042b71", "IPY_MODEL_fa3b7f0f51bf44a4bc4251926ec281f9", "IPY_MODEL_37c7f64cb56349dda6e5fa16c6938a78"], "layout": "IPY_MODEL_073e0cca9a8f4faea1aa025a2bfbecce"}}, "c1b6571507854bce8a4bf420fd83e322": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e990cedc6aa14e419de43bbd690e849c", "style": "IPY_MODEL_7ef27e42d50e49238f5c63db755bc37c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e990cedc6aa14e419de43bbd690e849c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ef27e42d50e49238f5c63db755bc37c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dfaf2cbd86ac4564a334ddcc2be030f3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0ee9273eff06428fa574f46aee9fad11", "style": "IPY_MODEL_55c8405e2f0c447c97b78d7213ec0408", "value": "<div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. 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"#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": "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/"}]}, "fa3b7f0f51bf44a4bc4251926ec281f9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7bbce7e647ab4e6b91d16c36d6d427ad", "style": "IPY_MODEL_52be4f31ec7542feb3d1a534e72246eb", "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>"}}, "7bbce7e647ab4e6b91d16c36d6d427ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52be4f31ec7542feb3d1a534e72246eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "37c7f64cb56349dda6e5fa16c6938a78": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_03550a4e7f054b7ea9bc06b0741e2154", "style": "IPY_MODEL_55132418e9934480b572af8ca3e25828", "value": "<br><a href=\"#summary_3O18A5UR7KO64T026RJRNY204-section-JLQM5\" style=\"font-size: 14px;\">Go to top</a>"}}, "03550a4e7f054b7ea9bc06b0741e2154": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55132418e9934480b572af8ca3e25828": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "073e0cca9a8f4faea1aa025a2bfbecce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "363a3d875b544b32bba756a81a88e547": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd80102419e3479ca4705f654e1ae4a3": {"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_882043bd14d0498e80cb6ba97676f7ac", "IPY_MODEL_95318b027c3f459fae1dda1c607e1b1e", "IPY_MODEL_58e63d5749cd42a980c5560330167795"], "layout": "IPY_MODEL_26c7ef11421d41c1807423dfd0c2a890"}}, "882043bd14d0498e80cb6ba97676f7ac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5cc0d1904577423484855c3bc112da30", "style": "IPY_MODEL_c10670fbcb294059a19cf796d427f3fd", "value": "<h4 id=\"CalibrationMetric-TestDataset_3O18A5UR7KO64T026RJRNY204-section-JLQM5\"><b>Calibration Metric - Test Dataset</b></h4>"}}, "5cc0d1904577423484855c3bc112da30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c10670fbcb294059a19cf796d427f3fd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "95318b027c3f459fae1dda1c607e1b1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47265aa357434047a4263bfd9ba39e74", "style": "IPY_MODEL_df84fd33898140ada89913e11adbbf3e", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "47265aa357434047a4263bfd9ba39e74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df84fd33898140ada89913e11adbbf3e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58e63d5749cd42a980c5560330167795": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1e0322b1148e487fbba6fe2d41a77dac", "IPY_MODEL_21277502c53d4c9ebebaee8c578b7f14", "IPY_MODEL_87440aa430e347e5a9753de4fe408ce2", "IPY_MODEL_78662f2506794a2d89c1ce429b964419", "IPY_MODEL_08edf63310f2441cbdcb1f3fa01bb408"], "layout": "IPY_MODEL_1c28d0aad7fa4b388c7a8e90ddb5e59a"}}, "1e0322b1148e487fbba6fe2d41a77dac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a3be80163ec045f487f95a6e4b1bb072", "style": "IPY_MODEL_71cd28fcf27945f69914b267a4499a13", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a3be80163ec045f487f95a6e4b1bb072": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71cd28fcf27945f69914b267a4499a13": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "21277502c53d4c9ebebaee8c578b7f14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_da9b8ed0d18c4c9d93281a44b25dc217", "style": "IPY_MODEL_0b62e45f2f5c435db8a18453d0b6fafe", "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>"}}, "da9b8ed0d18c4c9d93281a44b25dc217": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0b62e45f2f5c435db8a18453d0b6fafe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "87440aa430e347e5a9753de4fe408ce2": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "1b0b9352-dbee-406e-8f36-0ff2f22be735"}, {"mode": "lines+markers", "name": "(brier:   0.0242)", "x": {"dtype": "float64", "shape": [6]}, "y": {"dtype": "float64", "shape": [6]}, "type": "scatter", "uid": "584211b1-4a96-44c3-a1bb-e4a4d7e07fe2"}], "_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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Try increasing n_samples or supply additional features.</td>\n    </tr>\n    <tr>\n      <td id=\"T_80c63_row1_col0\" class=\"data row1 col0\" >Weak Segments Performance - Test Dataset</td>\n      <td id=\"T_80c63_row1_col1\" class=\"data row1 col1\" >DeepchecksProcessError: WeakSegmentsPerformance was unable to train an error model to find weak segments. Try increasing n_samples or supply additional features.</td>\n    </tr>\n    <tr>\n      <td id=\"T_80c63_row2_col0\" class=\"data row2 col0\" >Regression Error Distribution - Train Dataset</td>\n      <td id=\"T_80c63_row2_col1\" class=\"data row2 col1\" >Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_80c63_row3_col0\" class=\"data row3 col0\" >Regression Error Distribution - Test Dataset</td>\n      <td id=\"T_80c63_row3_col1\" class=\"data row3 col1\" >Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_80c63_row4_col0\" class=\"data row4 col0\" >Boosting Overfit</td>\n      <td id=\"T_80c63_row4_col1\" class=\"data row4 col1\" >Check is relevant for Boosting models of type ('AdaBoostClassifier', 'GradientBoostingClassifier', 'LGBMClassifier', 'XGBClassifier', 'CatBoostClassifier', 'AdaBoostRegressor', 'GradientBoostingRegressor', 'LGBMRegressor', 'XGBRegressor', 'CatBoostRegressor'), but received model of type LogisticRegression</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e0c7b96cd32643d39342dbe848fa27ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2db55d67040348fab35179be70d91a79": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "652712482c79476ab495f2b62b158b02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09f958e03122441f8004781d6cf325e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e9491d7f70d3468b9751c0af28a147b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0872485b0b0440fba8dedcbfbd6ff413": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
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    <br />

.. 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:
    |###        | 3/11 [Time: 00:00, Check=Confusion Matrix Report]
    Model Evaluation Suite:
    |#####      | 5/11 [Time: 00:00, Check=Simple Model Comparison]One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]

    Model Evaluation Suite:
    |#####      | 5/11 [Time: 00:16, Check=Weak Segments Performance]One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]

    Model Evaluation Suite:
    |######     | 6/11 [Time: 00:22, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |#########  | 9/11 [Time: 00:22, Check=Unused Features]          
                                                                               

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    {"version_major": 2, "version_minor": 0, "state": {"3f3bda02f1b24d5eb4669ec7d77134ca": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Model Evaluation Suite"}, "children": ["IPY_MODEL_4427042c035d4140b687318b0a0168da"], "layout": "IPY_MODEL_9f4aac626adb4dd397a8cd4c83542de8"}}, "4427042c035d4140b687318b0a0168da": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_98375d44a3094590b6338460ddf769f6", "IPY_MODEL_3324c1fb488f4e638c541cd15457dff5", "IPY_MODEL_c9fb390ff0f0417f9053c6c3a1eaee50", "IPY_MODEL_69562574a12f425db105d20728d0e19f", "IPY_MODEL_c8c82f6d55484c7f9afc9af424e1e405"], "layout": "IPY_MODEL_304a05e125ef42a8b7b372cc3a59cbf9"}}, "98375d44a3094590b6338460ddf769f6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_513a8babe82847fb936e1589a73e11e7", "style": "IPY_MODEL_69b315eed34e4c1b9a925bc34591368e", "value": "\n            <h1 id=\"summary_K8P4Q0B8IC8BBI4WIRE8QJNV8\">Model Evaluation Suite</h1>\n            <p>\n                The suite is composed of various checks such as: Model Inference Time, Roc Report, Weak Segments Performance, etc...<br>\n                Each check may contain conditions (which will result in pass / fail / warning / error\n                , represented by \n<span style=\"color: green;display:inline-block\">\u2713</span> /\n<span style=\"color: red;display:inline-block\">\u2716</span> /\n<span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> /\n<span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>\n) as well as other outputs such as plots or tables.<br>\n                Suites, checks and conditions can all be modified. Read more about\n                <a href=https://docs.deepchecks.com/en/stable/examples/guides/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"}}, "513a8babe82847fb936e1589a73e11e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69b315eed34e4c1b9a925bc34591368e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3324c1fb488f4e638c541cd15457dff5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3d8f4619f7ca4f278c90fe40c89a2e40", "IPY_MODEL_8d4702fd8f3d475bb8edfad493ab3b2a"], "layout": "IPY_MODEL_dc1ae1ad2a174d90bd209d33ec4f1ee9"}}, "3d8f4619f7ca4f278c90fe40c89a2e40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_243a9028187c4f91b0a13758ab15f4f6", "style": "IPY_MODEL_f2559faeed4b445b8014b5b43feb7100", "value": "<span id=\"summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-Z2FLU\"></span>"}}, "243a9028187c4f91b0a13758ab15f4f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2559faeed4b445b8014b5b43feb7100": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d4702fd8f3d475bb8edfad493ab3b2a": {"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_c51032e892204fd3b48a3c5d2235a365"], "layout": "IPY_MODEL_3c8c8bc19ec941b6982707dd964c3c0a", "selected_index": null}}, "c51032e892204fd3b48a3c5d2235a365": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bd8d170b749448c4b0efae010c8cb992", "IPY_MODEL_0fa0d3364325474494f249d807dcbd42", "IPY_MODEL_98162e6dba6b40f39029ba9108792809", "IPY_MODEL_147f5629dcf64fa7aca9ec631f9c1d2f", "IPY_MODEL_19287fc59a0d404e8b5c0e5a4490f6ed"], "layout": "IPY_MODEL_a908877030224884bd2980253e97a7a0"}}, "bd8d170b749448c4b0efae010c8cb992": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b6581858f714072a145d5aacd7cd7db", "style": "IPY_MODEL_361f0c017b354eec9c8420e5ad292d45", "value": "<style type=\"text/css\">\n#T_3704a_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3704a_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3704a_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3704a_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3704a_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3704a_\">\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_3704a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_3704a_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestPerformance_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-Z2FLU\">Train Test Performance</a></td>\n      <td id=\"T_3704a_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_3704a_row0_col3\" class=\"data row0 col3\" >3 scores failed. Found max degradation of 21.11% for metric Precision and class 1.</td>\n    </tr>\n    <tr>\n      <td id=\"T_3704a_row1_col0\" class=\"data row1 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_3704a_row1_col1\" class=\"data row1 col1\" ><a href=\"#UnusedFeatures-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-Z2FLU\">Unused Features - Test Dataset</a></td>\n      <td id=\"T_3704a_row1_col2\" class=\"data row1 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_3704a_row1_col3\" class=\"data row1 col3\" >Found 6 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2b6581858f714072a145d5aacd7cd7db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "361f0c017b354eec9c8420e5ad292d45": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0fa0d3364325474494f249d807dcbd42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7d5e7f68b3aa434d8a8150e8923da3c7", "style": "IPY_MODEL_e3d30964c3384f448b5c85086d8e7ee5", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7d5e7f68b3aa434d8a8150e8923da3c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3d30964c3384f448b5c85086d8e7ee5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "98162e6dba6b40f39029ba9108792809": {"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_edea2b21d4514cd5a105ccdd539cc5cc", "IPY_MODEL_04fd2b5c9ec247098d2623d87cd1a9e4", "IPY_MODEL_48b4b109b6a74a65a55b7af3461f4fc9", "IPY_MODEL_471b7703f0a64761be7e7c9dd1bc4919"], "layout": "IPY_MODEL_40cbf4bedbc34ff49e2e90a9ba44a951"}}, "edea2b21d4514cd5a105ccdd539cc5cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0af0130217da4dc3b59af2afaee81570", "style": "IPY_MODEL_1bc84683586b4ac0be50e19fe7d78c4c", "value": "<h4 id=\"TrainTestPerformance_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-Z2FLU\"><b>Train Test Performance</b></h4>"}}, "0af0130217da4dc3b59af2afaee81570": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bc84683586b4ac0be50e19fe7d78c4c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04fd2b5c9ec247098d2623d87cd1a9e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dbf0d42284654972a20f481a9790712f", "style": "IPY_MODEL_68aa8a80277645e4830b3b161313c7f9", "value": "<p>Summarize given model performance on the train and test datasets based on selected scorers. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "dbf0d42284654972a20f481a9790712f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "68aa8a80277645e4830b3b161313c7f9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "48b4b109b6a74a65a55b7af3461f4fc9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_322bf4fbda774ad98043ce79b8b2b8db", "style": "IPY_MODEL_9ad3609953b84e3eb3fd963900b29282", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a693a_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a693a_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a693a_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a693a_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a693a_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a693a_\">\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_a693a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a693a_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_a693a_row0_col2\" class=\"data row0 col2\" >3 scores failed. Found max degradation of 21.11% for metric Precision and class 1.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "322bf4fbda774ad98043ce79b8b2b8db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ad3609953b84e3eb3fd963900b29282": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "471b7703f0a64761be7e7c9dd1bc4919": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d581099c4c2e433a9d8c7c4521dca851", "IPY_MODEL_a536c646a12441ff9238b6964c8634b4", "IPY_MODEL_3f9066b7e676466787f399562cb2b8e1"], "layout": "IPY_MODEL_e89979d1b76549f8ac94e5ad67d2776d"}}, "d581099c4c2e433a9d8c7c4521dca851": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1fbdfb3b069b479c8d6b7348cd481849", "style": "IPY_MODEL_e5b458cc52df4bbd83faf4d261f0509a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1fbdfb3b069b479c8d6b7348cd481849": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5b458cc52df4bbd83faf4d261f0509a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a536c646a12441ff9238b6964c8634b4": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "0bb57c6e-5d52-4cb1-b6b9-afbe83ec1113"}, {"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": "ef33cdff-e70f-4b74-a734-aa2633a3a2fd"}, {"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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"y", "buffer"], "data": "AAAAAAAA8D8AAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "k4I7h0vO7z/N1qHfbB3qPw=="}, {"encoding": "base64", "path": ["_data", 4, "y", "buffer"], "data": "M0Vsl5HX7z/pkz7pkz7pPw=="}, {"encoding": "base64", "path": ["_data", 5, "y", "buffer"], "data": "xa1Q3ArF7z/DMAzDMAzrPw=="}]}, "3f9066b7e676466787f399562cb2b8e1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_853885e3ef3e421196d4f32f24594a91", "style": "IPY_MODEL_d264795ded5640efaa8ca75dfa1ee505", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-Z2FLU\" style=\"font-size: 14px;\">Go to top</a>"}}, "853885e3ef3e421196d4f32f24594a91": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d264795ded5640efaa8ca75dfa1ee505": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e89979d1b76549f8ac94e5ad67d2776d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "40cbf4bedbc34ff49e2e90a9ba44a951": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "147f5629dcf64fa7aca9ec631f9c1d2f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d49794d965674d88ad870714ab4f07a6", "style": "IPY_MODEL_8ef6e6d452274eea8d2209fc95a4d74d", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "d49794d965674d88ad870714ab4f07a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ef6e6d452274eea8d2209fc95a4d74d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "19287fc59a0d404e8b5c0e5a4490f6ed": {"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_e0386c0ec1ae47638204abeb717e1d30", "IPY_MODEL_edf86390744541a380cfa7401f4c83cb", "IPY_MODEL_0b65910f734a491fb3b330138d1219a0", "IPY_MODEL_eaa189581d7b42a98e182ea153828907"], "layout": "IPY_MODEL_13fe6171fbb141849cf781e1eeb92263"}}, "e0386c0ec1ae47638204abeb717e1d30": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c8ccf58ae0b34bddb0cea2ebe2db0cff", "style": "IPY_MODEL_10ab123001c34aabb051fa2f3ddeeb4f", "value": "<h4 id=\"UnusedFeatures-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-Z2FLU\"><b>Unused Features - Test Dataset</b></h4>"}}, "c8ccf58ae0b34bddb0cea2ebe2db0cff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "10ab123001c34aabb051fa2f3ddeeb4f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "edf86390744541a380cfa7401f4c83cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8ec5ad95df0840c8806507649d9d7567", "style": "IPY_MODEL_f23e6d36ab874e0e91a2f5498335d7cf", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8ec5ad95df0840c8806507649d9d7567": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f23e6d36ab874e0e91a2f5498335d7cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b65910f734a491fb3b330138d1219a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ebdcc30ce9cd4e02a8c6a17f8c052cea", "style": "IPY_MODEL_4ed81c488ba644ca83be763cd115a6c2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_90e13_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90e13_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90e13_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90e13_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_90e13_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_90e13_\">\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_90e13_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_90e13_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_90e13_row0_col2\" class=\"data row0 col2\" >Found 6 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ebdcc30ce9cd4e02a8c6a17f8c052cea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ed81c488ba644ca83be763cd115a6c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eaa189581d7b42a98e182ea153828907": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_13dde9fd7f264902978dbe8f5add89e7", "IPY_MODEL_a55c3ccb03374c1aa15f19fb75765afe", "IPY_MODEL_71f2e702033c4a3b81e78afcb774ed11", "IPY_MODEL_a731afb353354795b7c97a47632dd33c"], "layout": "IPY_MODEL_e3ebd6fc0f694a62909a4197ea81dac6"}}, "13dde9fd7f264902978dbe8f5add89e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2604c3ff19dd4790980e344395feb450", "style": "IPY_MODEL_1f237423685847bc81b88190cb23ef0f", "value": "<h5><b>Additional Outputs</b></h5>"}}, "2604c3ff19dd4790980e344395feb450": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f237423685847bc81b88190cb23ef0f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a55c3ccb03374c1aa15f19fb75765afe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6ab6f34317f74a43b1050b931258be70", "style": "IPY_MODEL_aa6ec9df23be4888857def1403fd1a3f", "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>"}}, "6ab6f34317f74a43b1050b931258be70": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa6ec9df23be4888857def1403fd1a3f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "71f2e702033c4a3b81e78afcb774ed11": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "indianred"}, "name": "Feature Importance %", "orientation": "h", "x": {"dtype": "float64", "shape": [11]}, "y": ["urlLength", "sbr", "numDigits", "entropy", "specialChars", "scriptLength", "sscr", "ext_info", "numImages", "num_@", "hasHttp"], "type": "bar", "uid": "805cdfca-eb64-4c46-bc90-33b3b061ebbb"}, {"marker": {"color": "lightsalmon"}, "name": "Feature Variance %", "orientation": "h", "x": {"dtype": "float64", "shape": [11]}, "y": ["urlLength", "sbr", "numDigits", "entropy", "specialChars", "scriptLength", "sscr", "ext_info", "numImages", "num_@", "hasHttp"], "type": "bar", "uid": "2c042c2b-be11-4008-8ef2-15107831c82c"}], "_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, 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"IPY_MODEL_679f68d3ed654a33aa2e93ca483de4d6", "IPY_MODEL_7ddd168561e04c26abc55432376d4cc4", "IPY_MODEL_679f68d3ed654a33aa2e93ca483de4d6", "IPY_MODEL_36f841d717b34920864652fcce28abb4", "IPY_MODEL_679f68d3ed654a33aa2e93ca483de4d6", "IPY_MODEL_cd697e4f166341bd8c1e86ba6ebf9977", "IPY_MODEL_679f68d3ed654a33aa2e93ca483de4d6", "IPY_MODEL_b6091581dae8432c80926605a67ea0f2", "IPY_MODEL_679f68d3ed654a33aa2e93ca483de4d6", "IPY_MODEL_24cfebe77eb842c0a274084a922bad98"], "layout": "IPY_MODEL_d4b149b5e03b451c989f5d2f81306dc5"}}, "b13ccbfe729f4b678af73f2f1d4c3cf2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3fa867ac05ac4a528917a8b426be8371", "style": "IPY_MODEL_b5e6ad6700914e208add9340d9dcfec0", "value": "<style type=\"text/css\">\n#T_51211_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51211_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51211_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51211_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_51211_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_51211_\">\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_51211_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row0_col1\" class=\"data row0 col1\" ><a href=\"#ROCReport-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">ROC Report - Train Dataset</a></td>\n      <td id=\"T_51211_row0_col2\" class=\"data row0 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_51211_row0_col3\" class=\"data row0 col3\" >All classes passed, minimum AUC found is 1 for class 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_51211_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row1_col1\" class=\"data row1 col1\" ><a href=\"#ROCReport-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">ROC Report - Test Dataset</a></td>\n      <td id=\"T_51211_row1_col2\" class=\"data row1 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_51211_row1_col3\" class=\"data row1 col3\" >All classes passed, minimum AUC found is 0.92 for class 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_51211_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row2_col1\" class=\"data row2 col1\" ><a href=\"#TrainTestPredictionDrift_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">Train Test Prediction Drift</a></td>\n      <td id=\"T_51211_row2_col2\" class=\"data row2 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_51211_row2_col3\" class=\"data row2 col3\" >Found model prediction Earth Mover's Distance drift score of 2.20E-3</td>\n    </tr>\n    <tr>\n      <td id=\"T_51211_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row3_col1\" class=\"data row3 col1\" ><a href=\"#SimpleModelComparison_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">Simple Model Comparison</a></td>\n      <td id=\"T_51211_row3_col2\" class=\"data row3 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_51211_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_51211_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row4_col1\" class=\"data row4 col1\" ><a href=\"#UnusedFeatures-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">Unused Features - Train Dataset</a></td>\n      <td id=\"T_51211_row4_col2\" class=\"data row4 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_51211_row4_col3\" class=\"data row4 col3\" >Found 5 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_51211_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row5_col1\" class=\"data row5 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_51211_row5_col2\" class=\"data row5 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_51211_row5_col3\" class=\"data row5 col3\" >Found average inference time (seconds): 1.26e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_51211_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_51211_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_51211_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_51211_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 1.2e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3fa867ac05ac4a528917a8b426be8371": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5e6ad6700914e208add9340d9dcfec0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "271a119943654a9db856d5c62928b637": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3b09b13e58d5498a8c189ccb3832d1f3", "style": "IPY_MODEL_5d9ea50028494207895fc33fed877c2c", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "3b09b13e58d5498a8c189ccb3832d1f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d9ea50028494207895fc33fed877c2c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ba5d377cf0744dc5ac735177825cc774": {"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_0a8e7bd45c5b4295a21bce4bb64deb67", "IPY_MODEL_410723d577464e34b951514c54c898de", "IPY_MODEL_a61cdd3eda92415cb49e69444a21888c", "IPY_MODEL_93aaf820256d430487108e3bd111c364"], "layout": "IPY_MODEL_c368b0b04b284c2399a2b93752708d9a"}}, "0a8e7bd45c5b4295a21bce4bb64deb67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c532111862847699590909a8fbe0073", "style": "IPY_MODEL_91b33530d4164d128df8bced85d3ed66", "value": "<h4 id=\"ROCReport-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>ROC Report - Train Dataset</b></h4>"}}, "7c532111862847699590909a8fbe0073": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "91b33530d4164d128df8bced85d3ed66": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "410723d577464e34b951514c54c898de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58885561451c4a0a900006da6a92513f", "style": "IPY_MODEL_38819c8e83444d058d47553d2221eed8", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "58885561451c4a0a900006da6a92513f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38819c8e83444d058d47553d2221eed8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a61cdd3eda92415cb49e69444a21888c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_75fb9bcbd3dd45298a4788bc22f13e82", "style": "IPY_MODEL_21d72900d18a40f1bc2430ec9736a8d4", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_4b435_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4b435_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4b435_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4b435_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4b435_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_4b435_\">\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_4b435_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_4b435_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_4b435_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 1 for class 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "75fb9bcbd3dd45298a4788bc22f13e82": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "21d72900d18a40f1bc2430ec9736a8d4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93aaf820256d430487108e3bd111c364": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ed5793140001480db222dc59e6b2421e", "IPY_MODEL_96e27c63ae854de28178b850afa5a384", "IPY_MODEL_8dea8382450e4cecb51258a9fc432066", "IPY_MODEL_253b193feead407b9c11001f5a91789e"], "layout": "IPY_MODEL_ebca9df9a74541e98e2eda62ec3ed7f4"}}, "ed5793140001480db222dc59e6b2421e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a9009913fa3d4465ac56accdd209a9e0", "style": "IPY_MODEL_6ddec784f6ad47f8927dc86c7abc259d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a9009913fa3d4465ac56accdd209a9e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ddec784f6ad47f8927dc86c7abc259d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96e27c63ae854de28178b850afa5a384": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "auc = 1.00", "x": {"dtype": "float64", "shape": [3]}, "y": {"dtype": "float64", "shape": [3]}, "type": "scatter", "uid": "bbc84f96-8fde-461a-a64b-c4c72e5a310c"}, {"hovertemplate": "TPR: %{y:.2%}<br>FPR: %{x:.2%}<br>Youden's Index: 1.0", "marker": {"size": 15}, "mode": "markers", "showlegend": false, "x": [0.0], "y": [1.0], "type": "scatter", "uid": "bb76dd58-4290-44a2-96bb-2a60ff7cf1b4"}, {"line": {"color": "#444", "dash": "dash", "width": 2}, "showlegend": false, "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "34e1d54a-d370-40b4-9761-cfc7d7c38379"}], "_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"], 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[{"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"], 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"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": "Receiver operating characteristic for binary data"}, "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/"}]}, "8dea8382450e4cecb51258a9fc432066": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2644096ac04e4e668833e9515feebef8", "style": "IPY_MODEL_f26e0d58e7df4e488e7438ffb0ada167", "value": "<div><span style=\"font-size:0.8em\"><i>\n            The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined\n            as sensitivity + specificity - 1\n            </i></span></div>"}}, "2644096ac04e4e668833e9515feebef8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f26e0d58e7df4e488e7438ffb0ada167": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "253b193feead407b9c11001f5a91789e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d042bd4c129468a92dbd60b5cb00999", "style": "IPY_MODEL_e256749dc15243fea757ccf41150d04e", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "8d042bd4c129468a92dbd60b5cb00999": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e256749dc15243fea757ccf41150d04e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ebca9df9a74541e98e2eda62ec3ed7f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c368b0b04b284c2399a2b93752708d9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "679f68d3ed654a33aa2e93ca483de4d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aa70e5c2bef64204a5f4be36ff1ea136", "style": "IPY_MODEL_39963c6a64ca42859af9e8e79e6eeba9", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "aa70e5c2bef64204a5f4be36ff1ea136": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39963c6a64ca42859af9e8e79e6eeba9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6d43590886c14a04b5c47481acff1df1": {"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_d30e9e77b885449fb9932775afe9eb70", "IPY_MODEL_ab51db8a14cc4e048c45bbf5b1ae44d9", "IPY_MODEL_901d1c0e22a64db5bdf8116cea925a44", "IPY_MODEL_315c9d1d2fbc4bd886ce459dc3a6d9e0"], "layout": "IPY_MODEL_8fcbb0bc0ce241e0a8f382e0b0218759"}}, "d30e9e77b885449fb9932775afe9eb70": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3edac0448e1b499f815b36872a35d395", "style": "IPY_MODEL_195af71202e547b8a54fb93e8cfbd064", "value": "<h4 id=\"ROCReport-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>ROC Report - Test Dataset</b></h4>"}}, "3edac0448e1b499f815b36872a35d395": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "195af71202e547b8a54fb93e8cfbd064": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab51db8a14cc4e048c45bbf5b1ae44d9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9f1674bc7f74eb29e7281c1f7b22d55", "style": "IPY_MODEL_9bfda9872bb34c1199325d1c57e41a1c", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b9f1674bc7f74eb29e7281c1f7b22d55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9bfda9872bb34c1199325d1c57e41a1c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "901d1c0e22a64db5bdf8116cea925a44": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5798a6d201b34b3cbd9043344a484f39", "style": "IPY_MODEL_963f9f4b95184dc9acdd6a64be146d91", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_02de6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_02de6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_02de6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_02de6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_02de6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_02de6_\">\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_02de6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_02de6_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_02de6_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 0.92 for class 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5798a6d201b34b3cbd9043344a484f39": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "963f9f4b95184dc9acdd6a64be146d91": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "315c9d1d2fbc4bd886ce459dc3a6d9e0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b4f7b4ff846f40739344a4ff8fd0233e", "IPY_MODEL_0ac46a90a6c44070b9cacbcb569a4aef", "IPY_MODEL_967e36b66cf34a539a1d6dc15ae7075a", "IPY_MODEL_41d1f03249a94a2b82a4ff15aab45297"], "layout": "IPY_MODEL_0eb40bcc7442459ea1228e616aad8320"}}, "b4f7b4ff846f40739344a4ff8fd0233e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ada977dd5464821be8d8f229f2627bb", "style": "IPY_MODEL_231145c9c62e41f884f9733580cfb1d4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4ada977dd5464821be8d8f229f2627bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "231145c9c62e41f884f9733580cfb1d4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ac46a90a6c44070b9cacbcb569a4aef": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "auc = 0.92", "x": {"dtype": "float64", "shape": [3]}, "y": {"dtype": "float64", "shape": [3]}, "type": "scatter", "uid": "882c73db-0f87-4991-947b-0297b94530a3"}, {"hovertemplate": "TPR: %{y:.2%}<br>FPR: %{x:.2%}<br>Youden's Index: 1.0", "marker": {"size": 15}, "mode": "markers", "showlegend": false, "x": [0.15476190476190477], "y": [0.9928030303030303], "type": "scatter", "uid": "e9f76c82-dcc3-4e70-89d2-e04fec5d7221"}, {"line": {"color": "#444", "dash": "dash", "width": 2}, "showlegend": false, "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "013e3dcb-d199-4bfd-b47a-47b05073dc93"}], "_dom_classes": [], "_js2py_layoutDelta": {}, 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"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": 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"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": "Receiver operating characteristic for binary data"}, "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": "AAAAAAAAAAD0PM/zPM/DPwAAAAAAAPA/"}, {"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "AAAAAAAAAADFrVDcCsXvPwAAAAAAAPA/"}]}, "967e36b66cf34a539a1d6dc15ae7075a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_430913dff5e94695b2f2ffbe05894202", "style": "IPY_MODEL_3c6d123ecbc54ee3abb68586f0cf6a5c", "value": "<div><span style=\"font-size:0.8em\"><i>\n            The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined\n            as sensitivity + specificity - 1\n            </i></span></div>"}}, "430913dff5e94695b2f2ffbe05894202": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c6d123ecbc54ee3abb68586f0cf6a5c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "41d1f03249a94a2b82a4ff15aab45297": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c94bbdfc5bbe448ba059575072209699", "style": "IPY_MODEL_38b56fa7aec84bc1a76890ab3ddbf5f4", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "c94bbdfc5bbe448ba059575072209699": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38b56fa7aec84bc1a76890ab3ddbf5f4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0eb40bcc7442459ea1228e616aad8320": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8fcbb0bc0ce241e0a8f382e0b0218759": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ddd168561e04c26abc55432376d4cc4": {"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_43e61c72c2bc4be1b920bcfb284db032", "IPY_MODEL_ea2fe325db154f46a7927dc5ca1a71c0", "IPY_MODEL_8370dcf86c2240d0a82815b44ef2d160", "IPY_MODEL_36e1fef942984d51830d316876684312"], "layout": "IPY_MODEL_d6498813d0b84f20873e8166269083ac"}}, "43e61c72c2bc4be1b920bcfb284db032": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aebbb39957e1498ba05d57001cce8965", "style": "IPY_MODEL_842a052bcbb441baaf77155e6b4b056e", "value": "<h4 id=\"TrainTestPredictionDrift_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>Train Test Prediction Drift</b></h4>"}}, "aebbb39957e1498ba05d57001cce8965": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "842a052bcbb441baaf77155e6b4b056e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ea2fe325db154f46a7927dc5ca1a71c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ca17fac179574e219cb899e85aa852b3", "style": "IPY_MODEL_6dfd8fcd8cd34fd1a81234d6d1c138b8", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ca17fac179574e219cb899e85aa852b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6dfd8fcd8cd34fd1a81234d6d1c138b8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8370dcf86c2240d0a82815b44ef2d160": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a1c9db0c030c4ef2ad7be13493b8f2ec", "style": "IPY_MODEL_b3200b2214cd49b28418f85418c17977", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_26331_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26331_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26331_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26331_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_26331_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_26331_\">\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_26331_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_26331_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_26331_row0_col2\" class=\"data row0 col2\" >Found model prediction Earth Mover's Distance drift score of 2.20E-3</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a1c9db0c030c4ef2ad7be13493b8f2ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3200b2214cd49b28418f85418c17977": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "36e1fef942984d51830d316876684312": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5cf2ca1ede0e41158a8aeb3a5398e982", "IPY_MODEL_5621cecdd8f34823abbaa6b67613753b", "IPY_MODEL_e1f1294122b943bab4b7caca94afe18e", "IPY_MODEL_d9937c08f46640e5b1a13586fa445bd4", "IPY_MODEL_f4bc2a0c38c746d7b5129581000e0e25"], "layout": "IPY_MODEL_a53d43527f804dc3a8a5c723a9763f3a"}}, "5cf2ca1ede0e41158a8aeb3a5398e982": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48c4b8d9720646189a72f6c4f974d915", "style": "IPY_MODEL_381b6477e92f46e4bccd46f719899713", "value": "<h5><b>Additional Outputs</b></h5>"}}, "48c4b8d9720646189a72f6c4f974d915": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "381b6477e92f46e4bccd46f719899713": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5621cecdd8f34823abbaa6b67613753b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4501a363f4ca4627af810b672ebd238a", "style": "IPY_MODEL_39a677105819498fa73064c81cdcec20", "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>"}}, "4501a363f4ca4627af810b672ebd238a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39a677105819498fa73064c81cdcec20": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e1f1294122b943bab4b7caca94afe18e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_35228432f12a4c54ab0c4cba5f5ef1c6", "style": "IPY_MODEL_fe6fe01ce62c472da8f18ec707962816", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "35228432f12a4c54ab0c4cba5f5ef1c6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe6fe01ce62c472da8f18ec707962816": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d9937c08f46640e5b1a13586fa445bd4": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "356b3677-d8bc-42f0-bf7e-d4517430abd9"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": {"dtype": "float64", "shape": [2]}, "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "bar", "uid": "5a0d52d9-34e0-4c53-9102-542a56e7bbb3"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": {"dtype": "float64", "shape": [2]}, "xaxis": "x2", "y": {"dtype": "float64", "shape": [2]}, "yaxis": "y2", "type": "bar", "uid": "88a7bad1-4364-4ef3-82fb-728533c0b6f7"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score (Earth Mover's Distance)", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0000000000000002, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", 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{"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": "predicted probabilities for class 0", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "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": [-5.0, 6.0], "tickmode": "array", "ticks": "outside", "tickvals": {"dtype": "float64", "shape": [2]}}, "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, "x", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "dFNSHWID7z97kbVVvJOfPw=="}, {"encoding": "base64", "path": ["_data", 2, "x", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "s+tF1lbx7j/LRKGbkuqgPw=="}, {"encoding": "base64", "path": ["_layout", "xaxis2", "tickvals", "buffer"], "data": "AAAAAAAAAAAAAAAAAADwPw=="}]}, "f4bc2a0c38c746d7b5129581000e0e25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6306f818f2124a2790eb8a02d54bd795", "style": "IPY_MODEL_ad9faf3dfccb40b8859e78fbc9c44ed0", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "6306f818f2124a2790eb8a02d54bd795": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad9faf3dfccb40b8859e78fbc9c44ed0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a53d43527f804dc3a8a5c723a9763f3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d6498813d0b84f20873e8166269083ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36f841d717b34920864652fcce28abb4": {"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_b81e2d77c7e54aeaaa1718fefeabfa76", "IPY_MODEL_395e4552409646e1aef565ed4a8bcf96", "IPY_MODEL_85b0e0fb5ad942d1a471b4101a01c267", "IPY_MODEL_da97b65f04dc49a29cefffed2669ca81"], "layout": "IPY_MODEL_7b50d04203554428966b18202ca17f4e"}}, "b81e2d77c7e54aeaaa1718fefeabfa76": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8f7524363e847a19b4cdf4e09833d67", "style": "IPY_MODEL_bc151806b1344165be780d1ab3420056", "value": "<h4 id=\"SimpleModelComparison_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>Simple Model Comparison</b></h4>"}}, "d8f7524363e847a19b4cdf4e09833d67": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc151806b1344165be780d1ab3420056": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "395e4552409646e1aef565ed4a8bcf96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ea9f775366224b1aa121f57b431d39c1", "style": "IPY_MODEL_bf5bfaa9935a40329d864da7df718836", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ea9f775366224b1aa121f57b431d39c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf5bfaa9935a40329d864da7df718836": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85b0e0fb5ad942d1a471b4101a01c267": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1204a5bc75a642a5b01953cc485c2d10", "style": "IPY_MODEL_faf68629242c4b1baadddeb599549664", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_28597_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28597_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28597_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28597_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28597_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_28597_\">\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_28597_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_28597_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_28597_row0_col2\" class=\"data row0 col2\" >All classes passed, average gain for metrics: {'F1': '71.43%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1204a5bc75a642a5b01953cc485c2d10": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "faf68629242c4b1baadddeb599549664": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da97b65f04dc49a29cefffed2669ca81": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6b0530273ca644ef88ca07ce9d9743b5", "IPY_MODEL_18e3d915bb2e40b69a39a003ca25a0a6", "IPY_MODEL_d32b683a31304e3aba82ef42fe71869c"], "layout": "IPY_MODEL_2db5e6bd94574a029f81d820afda6f9b"}}, "6b0530273ca644ef88ca07ce9d9743b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3cb164c01a749119336ec47d4c66bea", "style": "IPY_MODEL_66b80df6aada4373ab8e160f30bae3eb", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c3cb164c01a749119336ec47d4c66bea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "66b80df6aada4373ab8e160f30bae3eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18e3d915bb2e40b69a39a003ca25a0a6": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "4d13f24a-fdfd-47a7-ba4a-e76bc86d9d7d"}, {"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": "d6285383-e898-4dbb-a150-a7d6ca37dae3"}], "_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=="}]}, "d32b683a31304e3aba82ef42fe71869c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1fb371e2a09248c79028fa112fff1a2e", "style": "IPY_MODEL_9ed52b01350f4bc4accf84af6578f9e9", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "1fb371e2a09248c79028fa112fff1a2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ed52b01350f4bc4accf84af6578f9e9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2db5e6bd94574a029f81d820afda6f9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7b50d04203554428966b18202ca17f4e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd697e4f166341bd8c1e86ba6ebf9977": {"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_197ae616fb4a4648b38e36e7a03e66ee", "IPY_MODEL_7ea2ca6111994099b89825d9b03478db", "IPY_MODEL_d55ace457c4b4383af932fae239e025c", "IPY_MODEL_61bd606d798c434e8b58ba34be1aeab1"], "layout": "IPY_MODEL_e0c0c1b9fcb84d23a8444a25fd5b05bc"}}, "197ae616fb4a4648b38e36e7a03e66ee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a1bfc9b054304aa8b79ed975cfe8ac42", "style": "IPY_MODEL_3e87969110dc45eb96ae575d74f4de64", "value": "<h4 id=\"UnusedFeatures-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>Unused Features - Train Dataset</b></h4>"}}, "a1bfc9b054304aa8b79ed975cfe8ac42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e87969110dc45eb96ae575d74f4de64": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7ea2ca6111994099b89825d9b03478db": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cac0c38e8cd6489485b90b067015c4bb", "style": "IPY_MODEL_cc0af58fa53c46d0bb7cf6f75984a155", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "cac0c38e8cd6489485b90b067015c4bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc0af58fa53c46d0bb7cf6f75984a155": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d55ace457c4b4383af932fae239e025c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcee8778def64166adf757ab03c7b5ae", "style": "IPY_MODEL_ed1e52aec87d46a68904b3c5bed490b2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_4e4f5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e4f5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e4f5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e4f5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e4f5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_4e4f5_\">\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_4e4f5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_4e4f5_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_4e4f5_row0_col2\" class=\"data row0 col2\" >Found 5 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "dcee8778def64166adf757ab03c7b5ae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed1e52aec87d46a68904b3c5bed490b2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "61bd606d798c434e8b58ba34be1aeab1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3a58c572380b42039e312bd47fd282b7", "IPY_MODEL_d4c5b923b2364a29a181214ff3e633dc", "IPY_MODEL_df24ca0f271f4eedb98e3377d2901c89", "IPY_MODEL_6113a53124854e61a3e7551bc330058b"], "layout": "IPY_MODEL_758dd32463eb48f0b56271cc607c962c"}}, "3a58c572380b42039e312bd47fd282b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a785e4d34d764bd0b40d511a5dfa6118", "style": "IPY_MODEL_ebacd648b2fe451786f7286683377a6b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a785e4d34d764bd0b40d511a5dfa6118": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ebacd648b2fe451786f7286683377a6b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d4c5b923b2364a29a181214ff3e633dc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b55e037b755a465db9f00235f2168ee6", "style": "IPY_MODEL_cafe2878550048c2bc1878d785e3c6b8", "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>"}}, "b55e037b755a465db9f00235f2168ee6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cafe2878550048c2bc1878d785e3c6b8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "df24ca0f271f4eedb98e3377d2901c89": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "7c44763d-08f6-4f41-bc07-6e0be1f0b8db"}, {"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": "2c51aff3-7e96-4c09-9529-3a90bfd9fcea"}], "_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": "JPnFDQbtCEBUPJp1u2/5P5VS5B06ogtAQTwfBDygB0Di030+XjgWQB9lK/vqDhtA2izdfH96FkDj5JtapM8SQPhfWAB8Ag1AdldNr/9HC0A="}]}, "6113a53124854e61a3e7551bc330058b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4326b37450a745e190f1c7918288c959", "style": "IPY_MODEL_f323245e1e31414f9ca1f7d15016138d", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "4326b37450a745e190f1c7918288c959": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f323245e1e31414f9ca1f7d15016138d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "758dd32463eb48f0b56271cc607c962c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e0c0c1b9fcb84d23a8444a25fd5b05bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6091581dae8432c80926605a67ea0f2": {"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_ad0b76c8ad454a569baa51a6dcbbac40", "IPY_MODEL_db5150cf091b4358a65c20e1288d7b8a", "IPY_MODEL_6a0a316b8d18460abe518b3ce13d9254", "IPY_MODEL_52a1486ac50341788ce7553edcb0f1d6"], "layout": "IPY_MODEL_4f1ac9f9178f4bc79fc165067a203483"}}, "ad0b76c8ad454a569baa51a6dcbbac40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6aa933c2e744dd980a548be18abc87b", "style": "IPY_MODEL_1bc3a612a74e42e69fdb8b6fed53bdcf", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "d6aa933c2e744dd980a548be18abc87b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bc3a612a74e42e69fdb8b6fed53bdcf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db5150cf091b4358a65c20e1288d7b8a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd3416112c8e4e8a8b24541a0c6394ac", "style": "IPY_MODEL_74343366dfc14c84af63adedfbb50959", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "fd3416112c8e4e8a8b24541a0c6394ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "74343366dfc14c84af63adedfbb50959": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6a0a316b8d18460abe518b3ce13d9254": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f07f15394a23413db44014a480866e34", "style": "IPY_MODEL_ae76b9e08a7c43508cfe7b476d5dbb72", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c41c4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c41c4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c41c4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c41c4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c41c4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c41c4_\">\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_c41c4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c41c4_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_c41c4_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.26e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f07f15394a23413db44014a480866e34": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae76b9e08a7c43508cfe7b476d5dbb72": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "52a1486ac50341788ce7553edcb0f1d6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7b3044c7139e433395c0c43ad3953a31", "IPY_MODEL_42417786062e4fbe87f909f956f805d9", "IPY_MODEL_79334dbf9bb14dbaaf26bc5956d84821", "IPY_MODEL_068a97c5b103445798e69e70b13b8bce"], "layout": "IPY_MODEL_f230121bd123409cb3ec1be5d76e7dfb"}}, "7b3044c7139e433395c0c43ad3953a31": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_18c4d2d337934306a617926a13af355a", "style": "IPY_MODEL_402a38fe631a41adb4d2861cfb3f0fb6", "value": "<h5><b>Additional Outputs</b></h5>"}}, "18c4d2d337934306a617926a13af355a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "402a38fe631a41adb4d2861cfb3f0fb6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42417786062e4fbe87f909f956f805d9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_96e7673ccbd44ac0b886e273f0d1a233", "style": "IPY_MODEL_49a743375894403ca3f11ab4b4aa72fb", "value": "<div>Average model inference time for one sample (in seconds): 1.26e-06</div>"}}, "96e7673ccbd44ac0b886e273f0d1a233": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "49a743375894403ca3f11ab4b4aa72fb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79334dbf9bb14dbaaf26bc5956d84821": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b5c322dc9584b679f1d47564da311f1", "style": "IPY_MODEL_926bc3b714c4423eb65c964ad2027054", "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>"}}, "2b5c322dc9584b679f1d47564da311f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "926bc3b714c4423eb65c964ad2027054": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "068a97c5b103445798e69e70b13b8bce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1d1cf9e1c2a14299b9bdd20c646c1d6e", "style": "IPY_MODEL_ccac3b3773f44021a636c19709fdfeea", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "1d1cf9e1c2a14299b9bdd20c646c1d6e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ccac3b3773f44021a636c19709fdfeea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f230121bd123409cb3ec1be5d76e7dfb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f1ac9f9178f4bc79fc165067a203483": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "24cfebe77eb842c0a274084a922bad98": {"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_1407cc780ce444fe9446df4a4a9fe078", "IPY_MODEL_ab599e28b8f8443dbcfc44c3bf468726", "IPY_MODEL_eb07814b18464c129752d05316bc96c1", "IPY_MODEL_ab4645fa0ead4e168f910f1f8030133c"], "layout": "IPY_MODEL_2020727995924587a74d85b53b32a998"}}, "1407cc780ce444fe9446df4a4a9fe078": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0762b13a9935479788db55124bcb0a20", "style": "IPY_MODEL_fd5b18cf68244c75967c5fa617330ea9", "value": "<h4 id=\"ModelInferenceTime-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "0762b13a9935479788db55124bcb0a20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fd5b18cf68244c75967c5fa617330ea9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab599e28b8f8443dbcfc44c3bf468726": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_52d3121aaa784324b79e5437edb571d0", "style": "IPY_MODEL_4731d13adc0747d6b32337717dfc2ab1", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "52d3121aaa784324b79e5437edb571d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4731d13adc0747d6b32337717dfc2ab1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eb07814b18464c129752d05316bc96c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a854c2c5d3848d4aa04759b61b2c663", "style": "IPY_MODEL_7ec62e26541340f1aa1cc3c87fbf0f64", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_1b90b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1b90b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1b90b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1b90b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1b90b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1b90b_\">\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_1b90b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1b90b_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_1b90b_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.2e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4a854c2c5d3848d4aa04759b61b2c663": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ec62e26541340f1aa1cc3c87fbf0f64": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab4645fa0ead4e168f910f1f8030133c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1664da6ccb3149bc8dc80e673e9cfb42", "IPY_MODEL_93abd028b0a24ce7b93e3f42954b9ff0", "IPY_MODEL_e4f663973c8b4856a6bb549a45473e44", "IPY_MODEL_35f9f142033547b280c90c6115cf0721"], "layout": "IPY_MODEL_664596e947964302b82496d6cd80c105"}}, "1664da6ccb3149bc8dc80e673e9cfb42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_25fdaf71d6454c6592f45f8dc1f4f870", "style": "IPY_MODEL_1cd559efb4fb4288a528eee3eeabf394", "value": "<h5><b>Additional Outputs</b></h5>"}}, "25fdaf71d6454c6592f45f8dc1f4f870": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1cd559efb4fb4288a528eee3eeabf394": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93abd028b0a24ce7b93e3f42954b9ff0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8338ece3bd2441588d599ea6a15710a5", "style": "IPY_MODEL_8fea0fe9e8a843d6b112f9582c89a98c", "value": "<div>Average model inference time for one sample (in seconds): 1.2e-06</div>"}}, "8338ece3bd2441588d599ea6a15710a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8fea0fe9e8a843d6b112f9582c89a98c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4f663973c8b4856a6bb549a45473e44": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3c4ce9c13fc141c69127855d5b14d7f4", "style": "IPY_MODEL_d08dcba3be594ce39662f869cfefd96b", "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>"}}, "3c4ce9c13fc141c69127855d5b14d7f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d08dcba3be594ce39662f869cfefd96b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "35f9f142033547b280c90c6115cf0721": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2930cb52c1574ee89f60921ad397567d", "style": "IPY_MODEL_69c627ea428a4ef187f4d3702a5ffc49", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-7U6N7\" style=\"font-size: 14px;\">Go to top</a>"}}, "2930cb52c1574ee89f60921ad397567d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69c627ea428a4ef187f4d3702a5ffc49": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "664596e947964302b82496d6cd80c105": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2020727995924587a74d85b53b32a998": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d4b149b5e03b451c989f5d2f81306dc5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "940729232fba49a39ea88a0b150fb094": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad9ffe51e6c241738e605b0b070dd9a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69562574a12f425db105d20728d0e19f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7307d361dec94d048c99fdfaf4782f5a", "IPY_MODEL_e349435cd6964dc997739980646194fb"], "layout": "IPY_MODEL_aec2cb0d3fb44fa885a82332fa532f96"}}, "7307d361dec94d048c99fdfaf4782f5a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90626e0f7d17455994472dbafdf740d0", "style": "IPY_MODEL_6f8e4671569f422d8f33f2572d86cb60", "value": "<span id=\"summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\"></span>"}}, "90626e0f7d17455994472dbafdf740d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f8e4671569f422d8f33f2572d86cb60": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e349435cd6964dc997739980646194fb": {"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_8bd1ee185f714ef9b08135d54795c94e"], "layout": "IPY_MODEL_c0833aaffd514ab6967a00b010f5eef5", "selected_index": null}}, "8bd1ee185f714ef9b08135d54795c94e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cbf20dd9f1a84c20b320691bd5940e38", "IPY_MODEL_bac391b495e2464993115371a21b5aa6", "IPY_MODEL_43df856f6f5d447d89a464fa537067bb", "IPY_MODEL_156b81fa059b47dda23751875b1cb9af", "IPY_MODEL_5d2dd494a0b94e62bb0965349d988622", "IPY_MODEL_156b81fa059b47dda23751875b1cb9af", "IPY_MODEL_9427acfe2d5842e3b0b70979828bd365", "IPY_MODEL_156b81fa059b47dda23751875b1cb9af", "IPY_MODEL_d424a255cec146a8b22c54c99bab0925"], "layout": "IPY_MODEL_b089d41b7a204947a36171672defe495"}}, "cbf20dd9f1a84c20b320691bd5940e38": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_09d0ed3ca37e4e49bc49d1c70c44d2e1", "style": "IPY_MODEL_b794985728aa4e8bbe5607e2b2fe7193", "value": "<style type=\"text/css\">\n#T_1547d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1547d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1547d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1547d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1547d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1547d_\">\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_1547d_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrixReport-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\">Confusion Matrix Report - Train Dataset</a></td>\n      <td id=\"T_1547d_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_1547d_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrixReport-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\">Confusion Matrix Report - Test Dataset</a></td>\n      <td id=\"T_1547d_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_1547d_row2_col0\" class=\"data row2 col0\" ><a href=\"#CalibrationMetric-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\">Calibration Metric - Train Dataset</a></td>\n      <td id=\"T_1547d_row2_col1\" class=\"data row2 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_1547d_row3_col0\" class=\"data row3 col0\" ><a href=\"#CalibrationMetric-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\">Calibration Metric - Test Dataset</a></td>\n      <td id=\"T_1547d_row3_col1\" class=\"data row3 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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"}}, "09d0ed3ca37e4e49bc49d1c70c44d2e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b794985728aa4e8bbe5607e2b2fe7193": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bac391b495e2464993115371a21b5aa6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e7f37f844a343cdbc91e9733d10ff1c", "style": "IPY_MODEL_12c334fddf9843e886a51f7ef6576d47", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "2e7f37f844a343cdbc91e9733d10ff1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "12c334fddf9843e886a51f7ef6576d47": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "43df856f6f5d447d89a464fa537067bb": {"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_b58f83b1b54e4529a277913a2305cfe1", "IPY_MODEL_a1f37fa161c0410bb04f36c329cdb0da", "IPY_MODEL_3b75e04e99cb4ca4bbe522bf528285e8"], "layout": "IPY_MODEL_d078d5ca428f4da98d285359cb947bde"}}, "b58f83b1b54e4529a277913a2305cfe1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b71dc0c740948c9befde78239e59530", "style": "IPY_MODEL_9c815b30d54045ce851b053a99976f76", "value": "<h4 id=\"ConfusionMatrixReport-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\"><b>Confusion Matrix Report - Train Dataset</b></h4>"}}, "9b71dc0c740948c9befde78239e59530": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c815b30d54045ce851b053a99976f76": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1f37fa161c0410bb04f36c329cdb0da": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fa33eac108544aa78871b85ca659f48a", "style": "IPY_MODEL_d8eaf58bd0c14b4484db88d34350f120", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "fa33eac108544aa78871b85ca659f48a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8eaf58bd0c14b4484db88d34350f120": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b75e04e99cb4ca4bbe522bf528285e8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6ad1b25be4384fd5a6fc332c37018703", "IPY_MODEL_e9393889433e46348b2728d4aeb9f8cf", "IPY_MODEL_64d43c7159624133899e9144ddffba69"], "layout": "IPY_MODEL_42cc4b1f112942a881e81181faf4395a"}}, "6ad1b25be4384fd5a6fc332c37018703": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dda46fc9747442d193e25979c55d3ef5", "style": "IPY_MODEL_e84121096412477f95e9529144a387ed", "value": "<h5><b>Additional Outputs</b></h5>"}}, "dda46fc9747442d193e25979c55d3ef5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e84121096412477f95e9529144a387ed": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9393889433e46348b2728d4aeb9f8cf": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[8360, 0], [0, 266]], "texttemplate": "%{z}%<br>(%{text})", "x": [0, 1], "y": [0, 1], "z": [["100", "0"], ["0", "100"]], "type": "heatmap", "uid": "df75ede0-1d5a-40b8-85bf-62b208150ec2"}], "_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": "Percent Out of True Values (Count)"}, "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}}, "64d43c7159624133899e9144ddffba69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_76514179db3047c394d633c822cb1ab2", "style": "IPY_MODEL_90aed3ce28084c32b9393c5cf6810ca5", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\" style=\"font-size: 14px;\">Go to top</a>"}}, "76514179db3047c394d633c822cb1ab2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90aed3ce28084c32b9393c5cf6810ca5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42cc4b1f112942a881e81181faf4395a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d078d5ca428f4da98d285359cb947bde": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "156b81fa059b47dda23751875b1cb9af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2fd0ec6d1e284d1893bf7fd01a882b1c", "style": "IPY_MODEL_b9ebb5e69d2d4860a755ad0834e47c9a", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "2fd0ec6d1e284d1893bf7fd01a882b1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b9ebb5e69d2d4860a755ad0834e47c9a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d2dd494a0b94e62bb0965349d988622": {"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_62aea7908de94510ae08ffe894f6053b", "IPY_MODEL_b60df3d9a5984685ba46cedf83e70a9d", "IPY_MODEL_be5c13a61598464ea275b90dfa40a832"], "layout": "IPY_MODEL_297c1163dd5f417db14199029f425a74"}}, "62aea7908de94510ae08ffe894f6053b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e5cb951348674ca3a2f3d1b92c4ccdaa", "style": "IPY_MODEL_b06dcf66a9d94ffeba0b225f20c717f7", "value": "<h4 id=\"ConfusionMatrixReport-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\"><b>Confusion Matrix Report - Test Dataset</b></h4>"}}, "e5cb951348674ca3a2f3d1b92c4ccdaa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b06dcf66a9d94ffeba0b225f20c717f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b60df3d9a5984685ba46cedf83e70a9d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1defdd50bd53495d90dd4b6f83ce93a1", "style": "IPY_MODEL_0bfea76c9fcd47908d85e4b164d13e04", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1defdd50bd53495d90dd4b6f83ce93a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0bfea76c9fcd47908d85e4b164d13e04": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be5c13a61598464ea275b90dfa40a832": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5c74ff36fc6d4953a1e141c4dfa095c7", "IPY_MODEL_b0614172be7b49e2836c3ce554630b8d", "IPY_MODEL_6eaed6d037364a3f9beb091b1c04ea42"], "layout": "IPY_MODEL_21f38f03967d48ec805f16122f048164"}}, "5c74ff36fc6d4953a1e141c4dfa095c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b38973d29af9428ba7e845b69eb4e2bf", "style": "IPY_MODEL_da936ae2b5d243718eb3b75706f4872a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b38973d29af9428ba7e845b69eb4e2bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da936ae2b5d243718eb3b75706f4872a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b0614172be7b49e2836c3ce554630b8d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[2621, 19], [13, 71]], "texttemplate": "%{z}%<br>(%{text})", "x": [0, 1], "y": [0, 1], "z": [["99.28", "0.72"], ["15.48", "84.52"]], "type": "heatmap", "uid": "20d2a37b-1983-4bbf-b56c-c7a3b4660777"}], "_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, 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"#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": "Percent Out of True Values (Count)"}, "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}}, "6eaed6d037364a3f9beb091b1c04ea42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a3c59b0cdab642fa8543500a04ad0423", "style": "IPY_MODEL_fc577ca2eb4d41f5afc0a44bc1964e8c", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\" style=\"font-size: 14px;\">Go to top</a>"}}, "a3c59b0cdab642fa8543500a04ad0423": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc577ca2eb4d41f5afc0a44bc1964e8c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "21f38f03967d48ec805f16122f048164": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "297c1163dd5f417db14199029f425a74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9427acfe2d5842e3b0b70979828bd365": {"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_e11d7bf56398420a8494df902e39cb60", "IPY_MODEL_16a2978c6c1748f2b87345fad693f919", "IPY_MODEL_53e2299a7b2940a0905c6c257aa2fd2e"], "layout": "IPY_MODEL_8f484d2095b344ccbdc907b296de9983"}}, "e11d7bf56398420a8494df902e39cb60": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_252c7c94270140f58f1026c4397b513d", "style": "IPY_MODEL_94b8597946a9493ca6ad6e7a83216938", "value": "<h4 id=\"CalibrationMetric-TrainDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\"><b>Calibration Metric - Train Dataset</b></h4>"}}, "252c7c94270140f58f1026c4397b513d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "94b8597946a9493ca6ad6e7a83216938": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "16a2978c6c1748f2b87345fad693f919": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6c9d83ee7f6345b4b38f66e011300a79", "style": "IPY_MODEL_071d9a07571f4971829b611dccdd0444", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6c9d83ee7f6345b4b38f66e011300a79": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "071d9a07571f4971829b611dccdd0444": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "53e2299a7b2940a0905c6c257aa2fd2e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2cd8505ac78b449ba555450feb7a2d57", "IPY_MODEL_2dad3feda8ce4bb1b3c7f03235b303fa", "IPY_MODEL_a9dd08bb9050457690c6ac4a0c33a15d", "IPY_MODEL_a885b5259b494383874aade84315c482", "IPY_MODEL_bdb3a4ea0d3642718eb9eddc44d37159"], "layout": "IPY_MODEL_f5e4a2cb55854cb7bc6b85cd1f12fbbb"}}, "2cd8505ac78b449ba555450feb7a2d57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f7d36c1dfdd435abdf33f780ecbb25b", "style": "IPY_MODEL_0a1f63aaecf5468e9a1b1e051402fa99", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4f7d36c1dfdd435abdf33f780ecbb25b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a1f63aaecf5468e9a1b1e051402fa99": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2dad3feda8ce4bb1b3c7f03235b303fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f4649a543de4d5d9cf13503f9649331", "style": "IPY_MODEL_5ed3b4f06dcf4e2cbdf8aa009f9abc19", "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>"}}, "4f4649a543de4d5d9cf13503f9649331": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ed3b4f06dcf4e2cbdf8aa009f9abc19": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9dd08bb9050457690c6ac4a0c33a15d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "570d3cc1-1193-4f75-987e-c0d56a6cbc23"}, {"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=="}]}, "a885b5259b494383874aade84315c482": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d844cced28a84df09766fa7292b51bbd", "style": "IPY_MODEL_74ce7cb06cf04ad0a27f283ab32b0561", "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>"}}, "d844cced28a84df09766fa7292b51bbd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "74ce7cb06cf04ad0a27f283ab32b0561": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bdb3a4ea0d3642718eb9eddc44d37159": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47294bd29b3a488b80fc49cd2c28e57f", "style": "IPY_MODEL_d64adf7d83ca48418c09bf1182fe4e78", "value": "<br><a href=\"#summary_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\" style=\"font-size: 14px;\">Go to top</a>"}}, "47294bd29b3a488b80fc49cd2c28e57f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d64adf7d83ca48418c09bf1182fe4e78": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5e4a2cb55854cb7bc6b85cd1f12fbbb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f484d2095b344ccbdc907b296de9983": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d424a255cec146a8b22c54c99bab0925": {"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_83a4c14b0af04190a3b9290c31cb1479", "IPY_MODEL_69cca36f66284743a26f831e991d84e8", "IPY_MODEL_7b643b0291bf43b1babb873f76645fc6"], "layout": "IPY_MODEL_4767cd0e3b974209ae89032e38b61a08"}}, "83a4c14b0af04190a3b9290c31cb1479": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_923343fdaa70458e9ba65b69aa80df19", "style": "IPY_MODEL_afdd159606104d0686a0620a53f03c8c", "value": "<h4 id=\"CalibrationMetric-TestDataset_K8P4Q0B8IC8BBI4WIRE8QJNV8-section-HV3IZ\"><b>Calibration Metric - Test Dataset</b></h4>"}}, "923343fdaa70458e9ba65b69aa80df19": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "afdd159606104d0686a0620a53f03c8c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "69cca36f66284743a26f831e991d84e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ea2c6c959792455eab2adfbde968568d", "style": "IPY_MODEL_61c7b1d69d4048df90364be4284a6181", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ea2c6c959792455eab2adfbde968568d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61c7b1d69d4048df90364be4284a6181": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7b643b0291bf43b1babb873f76645fc6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_27397fceeef34a6597420aa62d3e3b2e", "IPY_MODEL_3d7397ab50ad48fbb3e970025e0f5f0e", "IPY_MODEL_03b781dffd44455da3693a991c2b9009", "IPY_MODEL_091f02303fbc4625a5ade7bfee16bd07", "IPY_MODEL_31970bcfae014cfaa2e51bf75e35e84b"], "layout": "IPY_MODEL_51efb71e8288491cb3e1bf0a7dc63db9"}}, "27397fceeef34a6597420aa62d3e3b2e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f52cb73650534c5b9e43aa14397a97f8", "style": "IPY_MODEL_d0c5e8ca2a9d49f48724ae63023aa5fe", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f52cb73650534c5b9e43aa14397a97f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d0c5e8ca2a9d49f48724ae63023aa5fe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3d7397ab50ad48fbb3e970025e0f5f0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a20c883fd7b4b26a0488a6391954574", "style": "IPY_MODEL_b4874fb0ffc64446af52e730819517c8", "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>"}}, "0a20c883fd7b4b26a0488a6391954574": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4874fb0ffc64446af52e730819517c8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "03b781dffd44455da3693a991c2b9009": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "0e21f007-6eeb-43e8-8534-065b99a1d50f"}, {"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_ea00b_row1_col0\" class=\"data row1 col0\" >Weak Segments Performance - Test Dataset</td>\n      <td id=\"T_ea00b_row1_col1\" class=\"data row1 col1\" >DeepchecksProcessError: WeakSegmentsPerformance was unable to train an error model to find weak segments. Try increasing n_samples or supply additional features.</td>\n    </tr>\n    <tr>\n      <td id=\"T_ea00b_row2_col0\" class=\"data row2 col0\" >Regression Error Distribution - Train Dataset</td>\n      <td id=\"T_ea00b_row2_col1\" class=\"data row2 col1\" >Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_ea00b_row3_col0\" class=\"data row3 col0\" >Regression Error Distribution - Test Dataset</td>\n      <td id=\"T_ea00b_row3_col1\" class=\"data row3 col1\" >Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_ea00b_row4_col0\" class=\"data row4 col0\" >Boosting Overfit</td>\n      <td id=\"T_ea00b_row4_col1\" class=\"data row4 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"}}, "11a2f381cacd4f65abdde04418af76e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f252cbcd26d24beb9acb366830070f8a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "54d715e09b0548d8b92fd43e535132ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26f0e0dbd561411ba03d7d776e24767d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "304a05e125ef42a8b7b372cc3a59cbf9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f4aac626adb4dd397a8cd4c83542de8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
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    {"version_major": 2, "version_minor": 0, "model_id": "3f3bda02f1b24d5eb4669ec7d77134ca"}
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    <br />
    <br />

.. 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: 00:00, Check=Roc Report]            
    Model Evaluation Suite:
    |###        | 3/11 [Time: 00:01, Check=Confusion Matrix Report]
    Model Evaluation Suite:
    |####       | 4/11 [Time: 00:01, Check=Train Test Prediction Drift]
    Model Evaluation Suite:
    |#####      | 5/11 [Time: 00:01, Check=Simple Model Comparison]    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]
    One or more of the test scores are non-finite: [        nan         nan]

    Model Evaluation Suite:
    |######     | 6/11 [Time: 00:49, Check=Weak Segments Performance]
    Model Evaluation Suite:
    |#######    | 7/11 [Time: 00:49, Check=Calibration Score]        
    Model Evaluation Suite:
    |#########  | 9/11 [Time: 00:49, Check=Unused Features]  
    Model Evaluation Suite:
    |########## | 10/11 [Time: 00:52, Check=Boosting Overfit]
                                                                                 

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id=\"T_3f610_row2_col2\" class=\"data row2 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_3f610_row2_col3\" class=\"data row2 col3\" >Found 8 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "73b4d0020159438f976695f4ca95d4be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "84ccd69f98ad43d2b7e536d8622a8b76": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d2d1cfd9e69444ec9249b77d0e36afd1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5ad7758b433e4900be6e681c15c531f0", "style": "IPY_MODEL_c52d7f3ad93f4b2db26a067fbeefbdee", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "5ad7758b433e4900be6e681c15c531f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c52d7f3ad93f4b2db26a067fbeefbdee": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3353c42e31b64f47a9ce54006aada7c0": {"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_a54f28f7880e435da9cd8fe32d8d7561", "IPY_MODEL_237a543189c7456d827d29a2e6847b78", "IPY_MODEL_87425cac9a614916b57a58a2ff01b7c7", "IPY_MODEL_ea05b02f01f34fd0bd4c72abef48d0fb"], "layout": "IPY_MODEL_dd24d1a57e4c492c9bf2c4404144027a"}}, "a54f28f7880e435da9cd8fe32d8d7561": {"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/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "2abd06cf796d4610a3d78d7f7eaf213f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a738d99efaa477f811197595fa0a367": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "87425cac9a614916b57a58a2ff01b7c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9353c1b17b174f0987e9409d9fb97f53", "style": "IPY_MODEL_87c5337f4d3a4f9cb0ec30fe1822b428", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c62cf_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c62cf_ thead {\n  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Found max degradation of 14.29% for metric Recall and class 1.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9353c1b17b174f0987e9409d9fb97f53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "87c5337f4d3a4f9cb0ec30fe1822b428": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ea05b02f01f34fd0bd4c72abef48d0fb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_14b4abafc9464d15972d3345f8db8b80", "IPY_MODEL_9861e9487b244b15ae42d96d808cd4e7", "IPY_MODEL_ed580470d1c841c09c32e70508b15a02"], "layout": "IPY_MODEL_9fd35692d4114a548276d02d78a87698"}}, "14b4abafc9464d15972d3345f8db8b80": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": 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"y", "buffer"], "data": "AAAAAAAA8D8AAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "mfDlkYzi7z9t/gqzGkXsPw=="}, {"encoding": "base64", "path": ["_data", 4, "y", "buffer"], "data": "TCfCgtXa7z9UOrJnICrtPw=="}, {"encoding": "base64", "path": ["_data", 5, "y", "buffer"], "data": "6nWkXkfq7z/btm3btm3rPw=="}]}, "ed580470d1c841c09c32e70508b15a02": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cfc85841a950462e838adfd36fe26d9f", "style": "IPY_MODEL_6a49c37516ef4f4dbfbe0e82d6e70b5d", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-3VLI7\" style=\"font-size: 14px;\">Go to top</a>"}}, "cfc85841a950462e838adfd36fe26d9f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6a49c37516ef4f4dbfbe0e82d6e70b5d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9fd35692d4114a548276d02d78a87698": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd24d1a57e4c492c9bf2c4404144027a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bf2d8863f664d9288d7736728456ee7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5fa5713fa163426380f02de84fe6b682", "style": "IPY_MODEL_b4cca20a6e9e48db85e23defacdc7657", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "5fa5713fa163426380f02de84fe6b682": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4cca20a6e9e48db85e23defacdc7657": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9fab69fceec0469d8eba20b959a2bfc4": {"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_ad5ea3fa4e1047db80cb86c1e4e398bc", "IPY_MODEL_984290ad138a424198fc1138f332ce6f", "IPY_MODEL_112e51811f454c108f026c0a67623b7c", "IPY_MODEL_f5941e71579b4fbdba1dedff5b443c3a"], "layout": "IPY_MODEL_e23bca21f4fa403f911e8dd29029e641"}}, "ad5ea3fa4e1047db80cb86c1e4e398bc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3b1d4b60c754c79bf39eb5bbdec7dd7", "style": "IPY_MODEL_1f786b0fb5a14f10ad651aba1d823320", "value": "<h4 id=\"UnusedFeatures-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-3VLI7\"><b>Unused Features - Train Dataset</b></h4>"}}, "b3b1d4b60c754c79bf39eb5bbdec7dd7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f786b0fb5a14f10ad651aba1d823320": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "984290ad138a424198fc1138f332ce6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fbb018170d2e423fb578277714225958", "style": "IPY_MODEL_99ec9844612841a39fcb48b46e87a402", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "fbb018170d2e423fb578277714225958": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "99ec9844612841a39fcb48b46e87a402": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "112e51811f454c108f026c0a67623b7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c1a7f4f1d6a447d288ca92845a405db2", "style": "IPY_MODEL_194f07dfac8a4bdbaac0ede8375afd91", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_7b7e5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b7e5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b7e5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b7e5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7b7e5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7b7e5_\">\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_7b7e5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_7b7e5_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_7b7e5_row0_col2\" class=\"data row0 col2\" >Found 8 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c1a7f4f1d6a447d288ca92845a405db2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "194f07dfac8a4bdbaac0ede8375afd91": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5941e71579b4fbdba1dedff5b443c3a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_29219d6524104b0490bcc378c36ccc5d", "IPY_MODEL_5d5390ec7b814a90bb8e8d7491c4a08e", "IPY_MODEL_7fe22fc3f963496a8eae86d2f9112a4c", "IPY_MODEL_f08b9c91b9e142e5a326f39bbd94e3ae"], "layout": "IPY_MODEL_92d5adf692734aac8798c4806905f210"}}, "29219d6524104b0490bcc378c36ccc5d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_74cb5bcf37594afcb624ad17c8439856", "style": "IPY_MODEL_ceeee01fdbe744d69081c2a6116d0f21", "value": "<h5><b>Additional Outputs</b></h5>"}}, "74cb5bcf37594afcb624ad17c8439856": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ceeee01fdbe744d69081c2a6116d0f21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d5390ec7b814a90bb8e8d7491c4a08e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bd547c037a1942feb587b713559f298f", "style": "IPY_MODEL_070a3894f8004f80a06ab2f0bc9e1527", "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>"}}, "bd547c037a1942feb587b713559f298f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "070a3894f8004f80a06ab2f0bc9e1527": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7fe22fc3f963496a8eae86d2f9112a4c": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", 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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": "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": "JPnFDQbtCEBBPB8EPKAHQJVS5B06ogtAKcROOFEqEECIOsJ1vRbzP1LZ6ytHyRNAJRXGXAIQEEDaVUwwsHUOQPhfWAB8Ag1A8Rbq87NdDEAs9MZe0dwLQPCXzN/aUgtAdldNr/9HC0A="}]}, "f08b9c91b9e142e5a326f39bbd94e3ae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3ca83abbac584940a10fecf53c34dd49", "style": "IPY_MODEL_6f5fcec882844a3b885b69ef726a794e", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-3VLI7\" style=\"font-size: 14px;\">Go to top</a>"}}, "3ca83abbac584940a10fecf53c34dd49": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f5fcec882844a3b885b69ef726a794e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "92d5adf692734aac8798c4806905f210": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e23bca21f4fa403f911e8dd29029e641": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "007d735fee8c471382cce8f5f6248e9d": {"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_242d387bce5a470b82b38cb54f391d71", "IPY_MODEL_5d05b32f33574e29b78c58299c01d1a6", "IPY_MODEL_fb3930b0e6804cafa982024e32d60284", "IPY_MODEL_18708d3930864c8eb7e1409261c1666e"], "layout": "IPY_MODEL_90eea972fa7b467a9a0b7b2bde7a8bf2"}}, "242d387bce5a470b82b38cb54f391d71": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b2b3b3d69ce4191a12aac69bef686a5", "style": "IPY_MODEL_87e536d6436145f88bfbc11d74352532", "value": "<h4 id=\"UnusedFeatures-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-3VLI7\"><b>Unused Features - Test Dataset</b></h4>"}}, "9b2b3b3d69ce4191a12aac69bef686a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "87e536d6436145f88bfbc11d74352532": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d05b32f33574e29b78c58299c01d1a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67f48c1d4e0f477c9ef7c6e5fdd239a6", "style": "IPY_MODEL_8463e0005d114afaa6287fdbad8fd831", "value": "<p>Detect features that are nearly unused by the model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_unused_features.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "67f48c1d4e0f477c9ef7c6e5fdd239a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8463e0005d114afaa6287fdbad8fd831": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fb3930b0e6804cafa982024e32d60284": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3d6fc280ed9f455595d3d270f04748cb", "style": "IPY_MODEL_c870c477cfff46f4bad8090a38bb4ddf", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a3eb4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a3eb4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a3eb4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a3eb4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a3eb4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a3eb4_\">\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_a3eb4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_a3eb4_row0_col1\" class=\"data row0 col1\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_a3eb4_row0_col2\" class=\"data row0 col2\" >Found 8 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3d6fc280ed9f455595d3d270f04748cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c870c477cfff46f4bad8090a38bb4ddf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18708d3930864c8eb7e1409261c1666e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_20afc304a1bb4c5f94f391faa729f1b3", "IPY_MODEL_ad75974992104b4c90ccdb997b9eaa9d", "IPY_MODEL_012bf05c6a064b1ab35e8badfffb7926", "IPY_MODEL_fea7fc5a76674c02a48fc96c75d51769"], "layout": "IPY_MODEL_459c5bab3302433bbda0a49d0bf45d6b"}}, "20afc304a1bb4c5f94f391faa729f1b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f52a358c8ce94acea682ba8eff86fa2e", "style": "IPY_MODEL_66f4bf21681d40e7b6e0eaf76a70db41", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f52a358c8ce94acea682ba8eff86fa2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "66f4bf21681d40e7b6e0eaf76a70db41": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad75974992104b4c90ccdb997b9eaa9d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d44bb9642f5f47858aff1a66b1afb1e0", "style": "IPY_MODEL_6889fbc88bac4cbebc8826f279489a7e", "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>"}}, "d44bb9642f5f47858aff1a66b1afb1e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6889fbc88bac4cbebc8826f279489a7e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "012bf05c6a064b1ab35e8badfffb7926": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "03031ee6-60d7-49d9-b92b-393513880b72"}, {"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": "466e036c-bb84-42ad-942f-c5d075a6b438"}], "_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_37b6e7749a844ffa9a5ba249955a2235", "IPY_MODEL_0852a3c9599846109e771d873710f08f", "IPY_MODEL_37b6e7749a844ffa9a5ba249955a2235", "IPY_MODEL_e18c208317ed46bda3752eef6c8dbcb9", "IPY_MODEL_37b6e7749a844ffa9a5ba249955a2235", "IPY_MODEL_71fdbfa2284741e68871cfe3c6df72ac"], "layout": "IPY_MODEL_04f32888631c4f28939cbd30970b2d81"}}, "03ec48799a4742179f1545750b1e9982": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2bc2150ce82448ab9280189dd2631bdc", "style": "IPY_MODEL_b3dc6ac43345472e910a3f2ace471dd7", "value": "<style type=\"text/css\">\n#T_cd343_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cd343_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cd343_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cd343_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cd343_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_cd343_\">\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_cd343_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row0_col1\" class=\"data row0 col1\" ><a href=\"#ROCReport-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">ROC Report - Train Dataset</a></td>\n      <td id=\"T_cd343_row0_col2\" class=\"data row0 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_cd343_row0_col3\" class=\"data row0 col3\" >All classes passed, minimum AUC found is 1 for class 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_cd343_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row1_col1\" class=\"data row1 col1\" ><a href=\"#ROCReport-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">ROC Report - Test Dataset</a></td>\n      <td id=\"T_cd343_row1_col2\" class=\"data row1 col2\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_cd343_row1_col3\" class=\"data row1 col3\" >All classes passed, minimum AUC found is 0.98 for class 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_cd343_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row2_col1\" class=\"data row2 col1\" ><a href=\"#TrainTestPredictionDrift_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">Train Test Prediction Drift</a></td>\n      <td id=\"T_cd343_row2_col2\" class=\"data row2 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_cd343_row2_col3\" class=\"data row2 col3\" >Found model prediction Earth Mover's Distance drift score of 2.15E-3</td>\n    </tr>\n    <tr>\n      <td id=\"T_cd343_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row3_col1\" class=\"data row3 col1\" ><a href=\"#SimpleModelComparison_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">Simple Model Comparison</a></td>\n      <td id=\"T_cd343_row3_col2\" class=\"data row3 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_cd343_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_cd343_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row4_col1\" class=\"data row4 col1\" ><a href=\"#BoostingOverfit_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">Boosting Overfit</a></td>\n      <td id=\"T_cd343_row4_col2\" class=\"data row4 col2\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_cd343_row4_col3\" class=\"data row4 col3\" >Found score decline of -0.11%</td>\n    </tr>\n    <tr>\n      <td id=\"T_cd343_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row5_col1\" class=\"data row5 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_cd343_row5_col2\" class=\"data row5 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_cd343_row5_col3\" class=\"data row5 col3\" >Found average inference time (seconds): 1.775e-05</td>\n    </tr>\n    <tr>\n      <td id=\"T_cd343_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_cd343_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_cd343_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_cd343_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 1.767e-05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2bc2150ce82448ab9280189dd2631bdc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3dc6ac43345472e910a3f2ace471dd7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b1d970083c69467695a7d64d52b22337": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47a3aee739a24500ada80753e2613c36", "style": "IPY_MODEL_57561dcc3ec44605ac6b7490257b76f6", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "47a3aee739a24500ada80753e2613c36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57561dcc3ec44605ac6b7490257b76f6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5e56c29f659f41f8b91ffea073126bfc": {"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_c2b875b14cb24c858bcc23236692aa15", "IPY_MODEL_7e6e05e33373401a9e42a5feea1b7d2f", "IPY_MODEL_54e8fbe149e14f75a09172760ab90a23", "IPY_MODEL_4bf569ad18184a13984f87549312890a"], "layout": "IPY_MODEL_c0f9db0f0ce141da8127bf133972c027"}}, "c2b875b14cb24c858bcc23236692aa15": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4791fa4ae870416c9bcac18aa5f9945c", "style": "IPY_MODEL_0c282c67239c44a6856774ac49a1cc6e", "value": "<h4 id=\"ROCReport-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>ROC Report - Train Dataset</b></h4>"}}, "4791fa4ae870416c9bcac18aa5f9945c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c282c67239c44a6856774ac49a1cc6e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7e6e05e33373401a9e42a5feea1b7d2f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6b3de5e12364bb2a93793db84bcaf39", "style": "IPY_MODEL_9e869c01cb604a249fe5987d99f1c618", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e6b3de5e12364bb2a93793db84bcaf39": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e869c01cb604a249fe5987d99f1c618": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "54e8fbe149e14f75a09172760ab90a23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce0c1b2bc5ab479098180678fe30dd8c", "style": "IPY_MODEL_17ec5e70298145b5b99fc9a93642051c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_06994_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_06994_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_06994_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_06994_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_06994_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_06994_\">\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_06994_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_06994_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_06994_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 1 for class 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ce0c1b2bc5ab479098180678fe30dd8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "17ec5e70298145b5b99fc9a93642051c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4bf569ad18184a13984f87549312890a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_715b2f1443474e22a816cef802d88d2a", "IPY_MODEL_75c9ee18c74b42a197eed69e2ee9f9e1", "IPY_MODEL_a4d64d6e7d6d4c4aae46313845ae2b60", "IPY_MODEL_1b093c1a560545ebb909fdf0635f2985"], "layout": "IPY_MODEL_0144942ae56c45b1ae780f3f9352988a"}}, "715b2f1443474e22a816cef802d88d2a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c193ac882eb049cfb26cc9ae0775d451", "style": "IPY_MODEL_e7ea26ff3f8e4665bcfcc7d995e94bde", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c193ac882eb049cfb26cc9ae0775d451": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e7ea26ff3f8e4665bcfcc7d995e94bde": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "75c9ee18c74b42a197eed69e2ee9f9e1": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"width": 2}, "name": "auc = 1.00", "x": {"dtype": "float64", "shape": [20]}, "y": {"dtype": "float64", "shape": [20]}, "type": "scatter", "uid": "bc266bf3-a902-48ac-adc4-088ee03715fd"}, {"hovertemplate": "TPR: %{y:.2%}<br>FPR: %{x:.2%}<br>Youden's Index: 1.0", "marker": {"size": 15}, "mode": "markers", 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"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"], 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"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, 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"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 for binary data"}, "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": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAN8cfXP0zcE/RSwVsVTEwj/4M+HPhD/DP15DeQ3lNcQ/KmKpiKUixj+QcUHGBRnHP6mIpSKWisg/KK+hvIbyyj9a3Wl1p9XNP6AJfyb8mdA/qzut7rS60z8AAAAAAADwPw=="}, {"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "AAAAAAAAAADtG9a+Ye3vP1S/55Y49e8/LSjsTC737z8HkfACJPnvP3PF8t0e+u8/umL5bg/97z8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPw=="}]}, "a4d64d6e7d6d4c4aae46313845ae2b60": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d9b16e00eeb342f8b907b06d7f3fe624", "style": "IPY_MODEL_3e92087d36674234b02d6b40a9128735", "value": "<div><span style=\"font-size:0.8em\"><i>\n            The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined\n            as sensitivity + specificity - 1\n            </i></span></div>"}}, "d9b16e00eeb342f8b907b06d7f3fe624": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e92087d36674234b02d6b40a9128735": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1b093c1a560545ebb909fdf0635f2985": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_493bc591ae854cfaa8919a289cf25f59", "style": "IPY_MODEL_c6e2357c19bc4ff7a23383d8c9f1a3e7", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\" style=\"font-size: 14px;\">Go to top</a>"}}, "493bc591ae854cfaa8919a289cf25f59": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6e2357c19bc4ff7a23383d8c9f1a3e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0144942ae56c45b1ae780f3f9352988a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c0f9db0f0ce141da8127bf133972c027": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37b6e7749a844ffa9a5ba249955a2235": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_baeafac082bf487db575e4c2039b428a", "style": "IPY_MODEL_cf94781ec0ab400687a6a4bdbec0315a", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "baeafac082bf487db575e4c2039b428a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf94781ec0ab400687a6a4bdbec0315a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4543610107794379b13823f3f57725ad": {"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_571b3ca4d774437da565da7d804ecb68", "IPY_MODEL_c7ef2e757a2e44b18b08b75810c33d21", "IPY_MODEL_ed19af4377f7484a86a070b89c487c1e", "IPY_MODEL_f3eeb85f6045456fadeead59cced3603"], "layout": "IPY_MODEL_053bf5c44e944a44a585faef8b2cb7ac"}}, "571b3ca4d774437da565da7d804ecb68": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10a2bd0ded174d448551129d951baa74", "style": "IPY_MODEL_3e09d500b6ea4db8bd90bebccd3e18f7", "value": "<h4 id=\"ROCReport-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>ROC Report - Test Dataset</b></h4>"}}, "10a2bd0ded174d448551129d951baa74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e09d500b6ea4db8bd90bebccd3e18f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c7ef2e757a2e44b18b08b75810c33d21": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f988c1c431da4b17845898c70d1a9db7", "style": "IPY_MODEL_36b5ff1bc3dd4ebf8268003611f385ea", "value": "<p>Calculate the ROC curve for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_roc_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f988c1c431da4b17845898c70d1a9db7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36b5ff1bc3dd4ebf8268003611f385ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed19af4377f7484a86a070b89c487c1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fe2b8c3229cd4e4088655acd6f77f109", "style": "IPY_MODEL_1848e55ec8b24465ac0a6a8254036308", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_95dcf_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_95dcf_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_95dcf_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_95dcf_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_95dcf_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_95dcf_\">\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_95dcf_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_95dcf_row0_col1\" class=\"data row0 col1\" >AUC score for all the classes is greater than 0.7</td>\n      <td id=\"T_95dcf_row0_col2\" class=\"data row0 col2\" >All classes passed, minimum AUC found is 0.98 for class 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "fe2b8c3229cd4e4088655acd6f77f109": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1848e55ec8b24465ac0a6a8254036308": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": 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"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 for binary data"}, "xaxis": {"title": {"text": "False Positive Rate"}}, "yaxis": {"title": {"text": "True Positive Rate"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, 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They are determined using Youden's index defined\n            as sensitivity + specificity - 1\n            </i></span></div>"}}, "6c0bc2dd2d2742c6be9cb43a26c7496b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d01276b64c84cc1a46258b70ebbb163": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "895c72caf9c84573a85ff8b751c458ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5362fcc818ec4721818df2af1910a317", "style": "IPY_MODEL_85b8ee432037471f881f8e2b98d131d3", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\" style=\"font-size: 14px;\">Go to top</a>"}}, "5362fcc818ec4721818df2af1910a317": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "85b8ee432037471f881f8e2b98d131d3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1b89c75afecc44e7a9424b457a2f3294": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "053bf5c44e944a44a585faef8b2cb7ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69cc434f0bf04c3ead8109ff600d7091": {"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_e02cb9f5b80740198b5b3e446b103690", "IPY_MODEL_354e8aabec0c438f8b14d902111b0283", "IPY_MODEL_e00cdfeeab8646bba8915846abe029e9", "IPY_MODEL_040b420f963041e9a7735a80ee566460"], "layout": "IPY_MODEL_3daa2072a15d45e1ba7e4ec026792a42"}}, "e02cb9f5b80740198b5b3e446b103690": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7da912659cc743f9977be59ac2a2ae39", "style": "IPY_MODEL_0afd745feb924d2fb907b969057da62f", "value": "<h4 id=\"TrainTestPredictionDrift_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>Train Test Prediction Drift</b></h4>"}}, "7da912659cc743f9977be59ac2a2ae39": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0afd745feb924d2fb907b969057da62f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "354e8aabec0c438f8b14d902111b0283": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f474b09fc99348f09cef28b21dc63d3a", "style": "IPY_MODEL_0e28a143cf14467fbb2fce01d894138a", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f474b09fc99348f09cef28b21dc63d3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e28a143cf14467fbb2fce01d894138a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e00cdfeeab8646bba8915846abe029e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5b2cc39670024d25a74545b6a0b84228", "style": "IPY_MODEL_c407b7bf6575457aa637ac39f4ec1fca", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_9c69b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9c69b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9c69b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9c69b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9c69b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9c69b_\">\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_9c69b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_9c69b_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_9c69b_row0_col2\" class=\"data row0 col2\" >Found model prediction Earth Mover's Distance drift score of 2.15E-3</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5b2cc39670024d25a74545b6a0b84228": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c407b7bf6575457aa637ac39f4ec1fca": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "040b420f963041e9a7735a80ee566460": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e029a589698d4761a93e48e893110dd2", "IPY_MODEL_80dac06fe83b4cb6860854a9c5816cb7", "IPY_MODEL_1d0c6fdadef1424dbd7a1b735ae40d5b", "IPY_MODEL_9b002d41b1984ec688564f3968f83d01", "IPY_MODEL_23ed6b1b352140ffb2a7e58a023cf703"], "layout": "IPY_MODEL_2198ecc0dbb442f3b08af27a23a41265"}}, "e029a589698d4761a93e48e893110dd2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_698a4ca583e6403e85e77ad441c91bd2", "style": "IPY_MODEL_2e29c63edd2b44d8919a6023a809241a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "698a4ca583e6403e85e77ad441c91bd2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e29c63edd2b44d8919a6023a809241a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "80dac06fe83b4cb6860854a9c5816cb7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_773e0d1fb84e401089dc51363009758a", "style": "IPY_MODEL_0828b847466f4ff8a583678b57da3619", "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>"}}, "773e0d1fb84e401089dc51363009758a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0828b847466f4ff8a583678b57da3619": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1d0c6fdadef1424dbd7a1b735ae40d5b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_18483b6759f84473ae57d83af01bfe54", "style": "IPY_MODEL_a2f7070ff21a4ff69f2d7363c983798c", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "18483b6759f84473ae57d83af01bfe54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2f7070ff21a4ff69f2d7363c983798c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9b002d41b1984ec688564f3968f83d01": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0021459001834385925], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "b8e1811f-0f7d-42de-8d03-4f9342c3b48e"}, {"fill": "tozeroy", "line": {"color": "#00008b", "shape": "spline"}, "name": "Train Dataset", "x": [4.1804361442923035e-22, 8.338441107119538e-21, 8.621822093699466e-21, 2.2975376048389732e-20, 2.383095287882442e-20, 2.796983179285065e-20, 2.796983179285065e-20, 2.796983179285065e-20, 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"23ed6b1b352140ffb2a7e58a023cf703": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c966dc658eb34ae796e13c570e63e9ff", "style": "IPY_MODEL_9078c418bf7742278f9381eadf433d2b", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\" style=\"font-size: 14px;\">Go to top</a>"}}, "c966dc658eb34ae796e13c570e63e9ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9078c418bf7742278f9381eadf433d2b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2198ecc0dbb442f3b08af27a23a41265": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3daa2072a15d45e1ba7e4ec026792a42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e560159a10d4f11a9475de95090ca3e": {"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_f4591bd057dd4a04ba704fe331a4f0e0", "IPY_MODEL_6060b5acc9594c9aa8f5dec5fcd48468", "IPY_MODEL_771ffe57e2fd4c1180ad05d5b82c3363", "IPY_MODEL_ce48b11718544f65a005179a2c6297fb"], "layout": "IPY_MODEL_9ea3f258bc5f4c6698bf8b219e47b9ef"}}, "f4591bd057dd4a04ba704fe331a4f0e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92adda057a834097b5f90e32f7ffe367", "style": "IPY_MODEL_4236d28f757e479c8a583422837a8a98", "value": "<h4 id=\"SimpleModelComparison_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>Simple Model Comparison</b></h4>"}}, "92adda057a834097b5f90e32f7ffe367": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4236d28f757e479c8a583422837a8a98": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6060b5acc9594c9aa8f5dec5fcd48468": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d198f1ae21bf4c97bfd2889fd8220d3b", "style": "IPY_MODEL_f8c92f699f44424e95fc43df67179dbc", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d198f1ae21bf4c97bfd2889fd8220d3b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f8c92f699f44424e95fc43df67179dbc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "771ffe57e2fd4c1180ad05d5b82c3363": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3c2448405a1431f9a2c1fe4865e5e53", "style": "IPY_MODEL_2de7c8f22a7f4e679e21543916a3b120", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b0293_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b0293_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b0293_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b0293_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b0293_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b0293_\">\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_b0293_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_b0293_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_b0293_row0_col2\" class=\"data row0 col2\" >All classes passed, average gain for metrics: {'F1': '82.69%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b3c2448405a1431f9a2c1fe4865e5e53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2de7c8f22a7f4e679e21543916a3b120": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ce48b11718544f65a005179a2c6297fb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2f9d965758a84eaab71cf478a59b64eb", "IPY_MODEL_ef2689ea35b642968cbba029f1fb86b4", "IPY_MODEL_fb985e08d1114ff18e8de483d8d842b3"], "layout": "IPY_MODEL_8ea7e0067fe248219f1f47ac757ba13d"}}, "2f9d965758a84eaab71cf478a59b64eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6882288419354fa1b1d240a0e6f164a5", "style": "IPY_MODEL_945c75863a294039a7f22b0a77856700", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6882288419354fa1b1d240a0e6f164a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "945c75863a294039a7f22b0a77856700": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef2689ea35b642968cbba029f1fb86b4": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "62f5d1bb-ac6e-42a9-8b49-9303e60e7957"}, {"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": "99cc270e-a5d2-4628-8999-91e5d3b47447"}], "_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=="}]}, "fb985e08d1114ff18e8de483d8d842b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ba445f755958486b8157645ad13c87a0", "style": "IPY_MODEL_ba3a7a7708e74cc687516b91d7787965", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\" style=\"font-size: 14px;\">Go to top</a>"}}, "ba445f755958486b8157645ad13c87a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ba3a7a7708e74cc687516b91d7787965": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8ea7e0067fe248219f1f47ac757ba13d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ea3f258bc5f4c6698bf8b219e47b9ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0852a3c9599846109e771d873710f08f": {"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_efa1227cddb74bcc857213810598bc37", "IPY_MODEL_202da2d22062420a8ea1b5dc017ac6ab", "IPY_MODEL_62f91b8785074b1da931bc5f98ddf316", "IPY_MODEL_d1e269f363434d1b9b09111896e2ac1b"], "layout": "IPY_MODEL_a17df5aed52b4358b283b531b9b69491"}}, "efa1227cddb74bcc857213810598bc37": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b0f08e893144a27a4006ed7ad692294", "style": "IPY_MODEL_350a7cecaac045428b2a5c257b442c2e", "value": "<h4 id=\"BoostingOverfit_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>Boosting Overfit</b></h4>"}}, "6b0f08e893144a27a4006ed7ad692294": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "350a7cecaac045428b2a5c257b442c2e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "202da2d22062420a8ea1b5dc017ac6ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_be9a9f19276f4d498e740ba692a0f0f2", "style": "IPY_MODEL_c15bb2b8592846f484d80c097fbffd21", "value": "<p>Check for overfit caused by using too many iterations in a gradient boosted model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_boosting_overfit.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "be9a9f19276f4d498e740ba692a0f0f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c15bb2b8592846f484d80c097fbffd21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "62f91b8785074b1da931bc5f98ddf316": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4843da0e84b446a7af4799ffcb102c1f", "style": "IPY_MODEL_ec56e0c4ada047609dfbe7fd8de18280", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_bf88d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bf88d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bf88d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bf88d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bf88d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_bf88d_\">\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_bf88d_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_bf88d_row0_col1\" class=\"data row0 col1\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_bf88d_row0_col2\" class=\"data row0 col2\" >Found score decline of -0.11%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4843da0e84b446a7af4799ffcb102c1f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec56e0c4ada047609dfbe7fd8de18280": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d1e269f363434d1b9b09111896e2ac1b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0e9868f6ad8e4fe39801cae5c49c3b6a", "IPY_MODEL_4f52d341cc84499f80d14b81fb98531c", "IPY_MODEL_93b0443f53fb44c9b08767bc95741108", "IPY_MODEL_78ee10e6cf8742b7bcbf03a82e1874b8"], "layout": "IPY_MODEL_b4350cd5e073470e9c54589d72a9c464"}}, "0e9868f6ad8e4fe39801cae5c49c3b6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c6226c3fb144fefb79b62e316d16364", "style": "IPY_MODEL_a93149c58aec4c5484f7c3c2ba77a58c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1c6226c3fb144fefb79b62e316d16364": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a93149c58aec4c5484f7c3c2ba77a58c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f52d341cc84499f80d14b81fb98531c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ddf5d2008cce443c99c44738f3deee39", "style": "IPY_MODEL_09fa7f1fd5eb4c67967fae2bb0ae8dba", "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>"}}, "ddf5d2008cce443c99c44738f3deee39": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09fa7f1fd5eb4c67967fae2bb0ae8dba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93b0443f53fb44c9b08767bc95741108": {"model_name": "FigureModel", 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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, 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"#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": 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"@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90876188527b44d7843a65099baf1527": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4350cd5e073470e9c54589d72a9c464": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a17df5aed52b4358b283b531b9b69491": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e18c208317ed46bda3752eef6c8dbcb9": {"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_26c8cf95aba8408db8c35be900c46768", "IPY_MODEL_29661b301dce4bce9617012267307f3d", "IPY_MODEL_c464889b8a7d448296e2712f9636feb2", "IPY_MODEL_894e3da19156452a8b70be919d791e7b"], "layout": "IPY_MODEL_30fdae1e8b6c4d2c8c034cca22cfefee"}}, "26c8cf95aba8408db8c35be900c46768": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aec33bebbb6d4404bb56f84979ef2625", "style": "IPY_MODEL_c996a9813eae43968fb07c889f238294", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "aec33bebbb6d4404bb56f84979ef2625": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c996a9813eae43968fb07c889f238294": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29661b301dce4bce9617012267307f3d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_639656587cf0419f8607998b4659f5e0", "style": "IPY_MODEL_ebdd2c05231a41b686af4913db492789", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "639656587cf0419f8607998b4659f5e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ebdd2c05231a41b686af4913db492789": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c464889b8a7d448296e2712f9636feb2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0faee20651844dcc9d54b79f3a0e957d", "style": "IPY_MODEL_e4a1e06d142743eb9bde958bd6f03947", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_04edb_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04edb_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04edb_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04edb_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04edb_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_04edb_\">\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_04edb_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_04edb_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_04edb_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.775e-05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0faee20651844dcc9d54b79f3a0e957d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4a1e06d142743eb9bde958bd6f03947": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "894e3da19156452a8b70be919d791e7b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_694975791909402c950bc1f44c1351e5", "IPY_MODEL_e11b0248ad284d2c926fc5054cb4022b", "IPY_MODEL_c837edc8bec94cd5abfba0db9edb796e", "IPY_MODEL_79751e870e934341b7372b820ad11311"], "layout": "IPY_MODEL_188dd2489c1f4d0db62dd791ae15def8"}}, "694975791909402c950bc1f44c1351e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f39d9b9122de4f1f8c7c36646dfc5354", "style": "IPY_MODEL_45f4030ac29d44e9bb29c3f4cf5b3dfc", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f39d9b9122de4f1f8c7c36646dfc5354": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45f4030ac29d44e9bb29c3f4cf5b3dfc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e11b0248ad284d2c926fc5054cb4022b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_375d5708421e4d2aa4747ec8b6d10b85", "style": "IPY_MODEL_03027d8ccbf3493eaa37ad4c0f2c94c5", "value": "<div>Average model inference time for one sample (in seconds): 1.775e-05</div>"}}, "375d5708421e4d2aa4747ec8b6d10b85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03027d8ccbf3493eaa37ad4c0f2c94c5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c837edc8bec94cd5abfba0db9edb796e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e1078159dbb34e0c8c9a31c8c4abb4ee", "style": "IPY_MODEL_9c463b55f42f489c8798c0a77d054920", "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>"}}, "e1078159dbb34e0c8c9a31c8c4abb4ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c463b55f42f489c8798c0a77d054920": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79751e870e934341b7372b820ad11311": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5add4da64c44313a4a8f4e82a9ebbda", "style": "IPY_MODEL_8191b23f6ca3422eb28a9cfaa590d410", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\" style=\"font-size: 14px;\">Go to top</a>"}}, "a5add4da64c44313a4a8f4e82a9ebbda": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8191b23f6ca3422eb28a9cfaa590d410": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "188dd2489c1f4d0db62dd791ae15def8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30fdae1e8b6c4d2c8c034cca22cfefee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71fdbfa2284741e68871cfe3c6df72ac": {"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_096c8182dc694d0285fb3a5fd66c4fb7", "IPY_MODEL_74af470d1c4c40699fb9ec9b2cb6ae3e", "IPY_MODEL_94e17c87e42a436594f7415f24e63c4d", "IPY_MODEL_eed1830a7ebc495abaaae76c5782868b"], "layout": "IPY_MODEL_f423887508304d4a840e02db41545fdc"}}, "096c8182dc694d0285fb3a5fd66c4fb7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_74d2d9ae3f32480ea60d365fc517b418", "style": "IPY_MODEL_52a6ac824a8e4a69a4d0d202122e31b9", "value": "<h4 id=\"ModelInferenceTime-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "74d2d9ae3f32480ea60d365fc517b418": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52a6ac824a8e4a69a4d0d202122e31b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "74af470d1c4c40699fb9ec9b2cb6ae3e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5737f443ba294954813a39d320f630a6", "style": "IPY_MODEL_065abc75a385408c90e3fec5e63fec4a", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5737f443ba294954813a39d320f630a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "065abc75a385408c90e3fec5e63fec4a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "94e17c87e42a436594f7415f24e63c4d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e656c780dfd24cfbad036aeaa255ec85", "style": "IPY_MODEL_672f4abf990740b9a0211998d0186c0c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_615f0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_615f0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_615f0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_615f0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_615f0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_615f0_\">\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_615f0_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_615f0_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_615f0_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 1.767e-05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e656c780dfd24cfbad036aeaa255ec85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "672f4abf990740b9a0211998d0186c0c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eed1830a7ebc495abaaae76c5782868b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_556dad7f85b6459da5015b3aa4a9cf7b", "IPY_MODEL_2b08aa6c4a1f434eba325d14e85edb9c", "IPY_MODEL_fbbf0086655240348375b8eb1ab27fe3", "IPY_MODEL_3e8bc9c4d02a47c5b8955c3ea6fc3916"], "layout": "IPY_MODEL_75c2dafb087a40699b1dfb0da9ccff7e"}}, "556dad7f85b6459da5015b3aa4a9cf7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0b1e671810f5437d91bb77d0bdbfa23b", "style": "IPY_MODEL_4d80926bde334fb5a52fdb0f75817ccf", "value": "<h5><b>Additional Outputs</b></h5>"}}, "0b1e671810f5437d91bb77d0bdbfa23b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d80926bde334fb5a52fdb0f75817ccf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2b08aa6c4a1f434eba325d14e85edb9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_403a5b061d1541568444383671d01162", "style": "IPY_MODEL_120df2fbfa13456885a9d792f163d58a", "value": "<div>Average model inference time for one sample (in seconds): 1.767e-05</div>"}}, "403a5b061d1541568444383671d01162": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "120df2fbfa13456885a9d792f163d58a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbbf0086655240348375b8eb1ab27fe3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2db06586d38044e1b575b3ea4fad3979", "style": "IPY_MODEL_6f516c9b624a454b81be248c9c928e79", "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>"}}, "2db06586d38044e1b575b3ea4fad3979": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f516c9b624a454b81be248c9c928e79": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e8bc9c4d02a47c5b8955c3ea6fc3916": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b235f5908bfa4cb5a65fbd4be845f893", "style": "IPY_MODEL_d0d976adb46041febb8ac511a62b32cd", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-KQ1UY\" style=\"font-size: 14px;\">Go to top</a>"}}, "b235f5908bfa4cb5a65fbd4be845f893": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d0d976adb46041febb8ac511a62b32cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "75c2dafb087a40699b1dfb0da9ccff7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f423887508304d4a840e02db41545fdc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "04f32888631c4f28939cbd30970b2d81": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c7979d9961c4d30845d29a13e068547": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d96d0c1126c0412db525646cc37c995e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3df50012c484a30b239d70451c756ee": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b0f1aab06db04fce989097eaa7e734ed", "IPY_MODEL_f6623049dfc04553995ba44475a761dd"], "layout": "IPY_MODEL_42e97d263b964bd7995da578641bc4be"}}, "b0f1aab06db04fce989097eaa7e734ed": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ef3c72673bd24ccaa3c52792cfaee44a", "style": "IPY_MODEL_035c997dc0ac498bbbd9fc53dc20bffb", "value": "<span id=\"summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\"></span>"}}, "ef3c72673bd24ccaa3c52792cfaee44a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "035c997dc0ac498bbbd9fc53dc20bffb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f6623049dfc04553995ba44475a761dd": {"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_4293238c9c3b4f35ac2a45afb099c007"], "layout": "IPY_MODEL_441e7949c31f44f8b51ada2427a1ff73", "selected_index": null}}, "4293238c9c3b4f35ac2a45afb099c007": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dcb9464ca67643cabb79bd38e7990444", "IPY_MODEL_5f3b537f9214471c87d58a563a353744", "IPY_MODEL_7d79b93fb5b44b0bb76abd8e058c5dec", "IPY_MODEL_d10461bf8274447a8b6995ab0ef41d78", "IPY_MODEL_c87b04fc5c3f4813baa554b6d534f0b9", "IPY_MODEL_d10461bf8274447a8b6995ab0ef41d78", "IPY_MODEL_3640d15084a24f54bd673c75797e535c", "IPY_MODEL_d10461bf8274447a8b6995ab0ef41d78", "IPY_MODEL_4ef16bc60c354e009345ef01884a9828"], "layout": "IPY_MODEL_0b216c92191c4ea180e615b24c35bf7c"}}, "dcb9464ca67643cabb79bd38e7990444": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fa50442ef6de4239a87780f183d8498a", "style": "IPY_MODEL_5f1b4dab56064400820556aad54acfbd", "value": "<style type=\"text/css\">\n#T_2eca1_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2eca1_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2eca1_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2eca1_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2eca1_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2eca1_\">\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_2eca1_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrixReport-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\">Confusion Matrix Report - Train Dataset</a></td>\n      <td id=\"T_2eca1_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_2eca1_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrixReport-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\">Confusion Matrix Report - Test Dataset</a></td>\n      <td id=\"T_2eca1_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_2eca1_row2_col0\" class=\"data row2 col0\" ><a href=\"#CalibrationMetric-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\">Calibration Metric - Train Dataset</a></td>\n      <td id=\"T_2eca1_row2_col1\" class=\"data row2 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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_2eca1_row3_col0\" class=\"data row3 col0\" ><a href=\"#CalibrationMetric-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\">Calibration Metric - Test Dataset</a></td>\n      <td id=\"T_2eca1_row3_col1\" class=\"data row3 col1\" >Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/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"}}, "fa50442ef6de4239a87780f183d8498a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f1b4dab56064400820556aad54acfbd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5f3b537f9214471c87d58a563a353744": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb3e0735561b4d028003f4ff80295d44", "style": "IPY_MODEL_d8a9dc27492b44a48e8df87edc2d6106", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "fb3e0735561b4d028003f4ff80295d44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8a9dc27492b44a48e8df87edc2d6106": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d79b93fb5b44b0bb76abd8e058c5dec": {"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_f9406a591302465ba7f50b3967a80e61", "IPY_MODEL_ee7afa041c1e46919cdde04b5cb0d3fa", "IPY_MODEL_e399ad7e6c044e6b9a1007b7af067dae"], "layout": "IPY_MODEL_95cdab0b5d9c4cb6970322d5213745a0"}}, "f9406a591302465ba7f50b3967a80e61": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d628da9a4c904da4a6bcf06eb517fdc9", "style": "IPY_MODEL_792ec3f1022c4aadacc73f9da5245224", "value": "<h4 id=\"ConfusionMatrixReport-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\"><b>Confusion Matrix Report - Train Dataset</b></h4>"}}, "d628da9a4c904da4a6bcf06eb517fdc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "792ec3f1022c4aadacc73f9da5245224": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee7afa041c1e46919cdde04b5cb0d3fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_08db9fe3f52b44e48bae4ba09dbeab5b", "style": "IPY_MODEL_0a510309ae53433b94bf141221774c89", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "08db9fe3f52b44e48bae4ba09dbeab5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a510309ae53433b94bf141221774c89": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e399ad7e6c044e6b9a1007b7af067dae": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c9b8dd1dfb034b0791d124f06c8a4e73", "IPY_MODEL_34b394965b694588ac42f6fcba4814a3", "IPY_MODEL_ddc45c54eb014b3bb03cdaa241c13117"], "layout": "IPY_MODEL_b98c2e2d446f4e88bbc90ff3d0a320b1"}}, "c9b8dd1dfb034b0791d124f06c8a4e73": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_75092d2764ff41278be9548dcbfbbe0d", "style": "IPY_MODEL_395e99fccdc34e5b927f2fec8345bc4c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "75092d2764ff41278be9548dcbfbbe0d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "395e99fccdc34e5b927f2fec8345bc4c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "34b394965b694588ac42f6fcba4814a3": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[8360, 0], [0, 266]], "texttemplate": "%{z}%<br>(%{text})", "x": [0, 1], "y": [0, 1], "z": [["100", "0"], ["0", "100"]], "type": "heatmap", "uid": "f5e6f538-47ae-4bb4-92bb-ae4c063894b7"}], "_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": "Percent Out of True Values (Count)"}, "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}}, "ddc45c54eb014b3bb03cdaa241c13117": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c19bec29bb384a8f9cac7111dad58071", "style": "IPY_MODEL_624dba208c5f403ca5fb900939089250", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\" style=\"font-size: 14px;\">Go to top</a>"}}, "c19bec29bb384a8f9cac7111dad58071": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "624dba208c5f403ca5fb900939089250": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b98c2e2d446f4e88bbc90ff3d0a320b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95cdab0b5d9c4cb6970322d5213745a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d10461bf8274447a8b6995ab0ef41d78": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b4d438c41e0f410a8612dba75f8e7f9b", "style": "IPY_MODEL_60637fe387964fd7a254a3444d33e9ab", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b4d438c41e0f410a8612dba75f8e7f9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "60637fe387964fd7a254a3444d33e9ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c87b04fc5c3f4813baa554b6d534f0b9": {"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_2151b8fe06c349aa9654ead309c7e558", "IPY_MODEL_3c6a63b03d8d4131b6d2d985afc5b977", "IPY_MODEL_756d1c0dcf044587a1cf642c1cab7387"], "layout": "IPY_MODEL_55ea42c69e19415189bb9b82a583d6ed"}}, "2151b8fe06c349aa9654ead309c7e558": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_583f6ab398dd449cbef738e525f1ffcb", "style": "IPY_MODEL_a16c8ca419304bdca1475594de3b475a", "value": "<h4 id=\"ConfusionMatrixReport-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\"><b>Confusion Matrix Report - Test Dataset</b></h4>"}}, "583f6ab398dd449cbef738e525f1ffcb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a16c8ca419304bdca1475594de3b475a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c6a63b03d8d4131b6d2d985afc5b977": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f4479a09551d4a83977003a55cf9d6bf", "style": "IPY_MODEL_9dc469567dd746628c7c6cc48e8c22f4", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f4479a09551d4a83977003a55cf9d6bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9dc469567dd746628c7c6cc48e8c22f4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "756d1c0dcf044587a1cf642c1cab7387": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2ecd1043b0a541398a418038d6cf887e", "IPY_MODEL_e4874c48b29047aaac0660d5c807028b", "IPY_MODEL_36f48366d6b84d66820096fffc9df1c1"], "layout": "IPY_MODEL_29ae19a0305c4625a22e95fe1876d0a0"}}, "2ecd1043b0a541398a418038d6cf887e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eeec29c8ae71473ca00dcd62ba2d4802", "style": "IPY_MODEL_92d9089e32f74ac98cdda0e609ae70fe", "value": "<h5><b>Additional Outputs</b></h5>"}}, "eeec29c8ae71473ca00dcd62ba2d4802": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "92d9089e32f74ac98cdda0e609ae70fe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4874c48b29047aaac0660d5c807028b": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"colorbar": {"title": {"text": "% out of<br>True Values"}}, "text": [[2633, 7], [12, 72]], "texttemplate": "%{z}%<br>(%{text})", "x": [0, 1], "y": [0, 1], "z": [["99.73", "0.27"], ["14.29", "85.71"]], "type": "heatmap", "uid": "e44e43d6-f627-4224-872b-1c20de7767fa"}], "_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": "Percent Out of True Values (Count)"}, "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}}, "36f48366d6b84d66820096fffc9df1c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_83bc99542546440db10a65dfad5f9ffc", "style": "IPY_MODEL_d1d9305801254cdb91558c59fe99112f", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\" style=\"font-size: 14px;\">Go to top</a>"}}, "83bc99542546440db10a65dfad5f9ffc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1d9305801254cdb91558c59fe99112f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29ae19a0305c4625a22e95fe1876d0a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55ea42c69e19415189bb9b82a583d6ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3640d15084a24f54bd673c75797e535c": {"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_a82abc8d346e40b2a94a27a599a9098b", "IPY_MODEL_cc062e0de3554ba984dec953fb59a790", "IPY_MODEL_70bd081f61384f4786f349364b880366"], "layout": "IPY_MODEL_6d4489f581494d1a839427321f78ddd1"}}, "a82abc8d346e40b2a94a27a599a9098b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_df7159c043664af6bdaecafabee9cde9", "style": "IPY_MODEL_e31af3e35056407296c2e06f61a3e873", "value": "<h4 id=\"CalibrationMetric-TrainDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\"><b>Calibration Metric - Train Dataset</b></h4>"}}, "df7159c043664af6bdaecafabee9cde9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e31af3e35056407296c2e06f61a3e873": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cc062e0de3554ba984dec953fb59a790": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d78995051224b8badd81c59b126b233", "style": "IPY_MODEL_3d12ea6d1aad44f29601a4ebfeead16e", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8d78995051224b8badd81c59b126b233": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d12ea6d1aad44f29601a4ebfeead16e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70bd081f61384f4786f349364b880366": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8fd6989234a44c4fad1815da2d5dbf87", "IPY_MODEL_e9fa960dc2d74fb095584ef5ca9058d3", "IPY_MODEL_5b6808b736d648ae8d360c3da7e6ccea", "IPY_MODEL_c89b45acf1fe432faa5b2985f817a8de", "IPY_MODEL_2450c87bb9e749fc8d1cf975b1220c9f"], "layout": "IPY_MODEL_5b9f65dc7f374956bec7155c9f86a438"}}, "8fd6989234a44c4fad1815da2d5dbf87": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f02ae5a9eb8462ebeb154b93d71a903", "style": "IPY_MODEL_f9dd5ce35a2846c4b7c40373c6b95eba", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3f02ae5a9eb8462ebeb154b93d71a903": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9dd5ce35a2846c4b7c40373c6b95eba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9fa960dc2d74fb095584ef5ca9058d3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1ab627b96fa64f2ea124eeb5494156d0", "style": "IPY_MODEL_b76b9c15891d49ff81aa7df3490331ba", "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>"}}, "1ab627b96fa64f2ea124eeb5494156d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b76b9c15891d49ff81aa7df3490331ba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5b6808b736d648ae8d360c3da7e6ccea": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "a7ad357a-5e52-4057-8505-f8d819dc9c5d"}, {"mode": "lines+markers", "name": "(brier:   0.0000)", "x": {"dtype": "float64", "shape": [2]}, "y": {"dtype": "float64", "shape": [2]}, "type": "scatter", "uid": "c706ec41-9287-47b8-9bd2-5c8a0c2f53e8"}], "_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, 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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=="}]}, "c89b45acf1fe432faa5b2985f817a8de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b89b51e26b9b47aaa45dc7369d8816c5", "style": "IPY_MODEL_b1057a8964c34e9baadde7456ad7e022", "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>"}}, "b89b51e26b9b47aaa45dc7369d8816c5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1057a8964c34e9baadde7456ad7e022": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2450c87bb9e749fc8d1cf975b1220c9f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_06a4bfe3f66a4eedab7aede9f67548e3", "style": "IPY_MODEL_b7b9a0e9f0844ba1bda4cc1ea1114f6b", "value": "<br><a href=\"#summary_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\" style=\"font-size: 14px;\">Go to top</a>"}}, "06a4bfe3f66a4eedab7aede9f67548e3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7b9a0e9f0844ba1bda4cc1ea1114f6b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5b9f65dc7f374956bec7155c9f86a438": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d4489f581494d1a839427321f78ddd1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ef16bc60c354e009345ef01884a9828": {"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_a24a5017fd3f44f199deeb26f20097f7", "IPY_MODEL_748e9871eeea4ad581e135047d750c6c", "IPY_MODEL_1a206f6c5cc5410a9162ed4202a4889e"], "layout": "IPY_MODEL_934c265745cc495abd95daff453f641c"}}, "a24a5017fd3f44f199deeb26f20097f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e98c3376b1f54ce8a534a3f1e774a30e", "style": "IPY_MODEL_3eb8b9bc0dbb4b6c97972503006e931c", "value": "<h4 id=\"CalibrationMetric-TestDataset_KY22UBTXCXAPRPN6QFGSXW71Q-section-MRLRM\"><b>Calibration Metric - Test Dataset</b></h4>"}}, "e98c3376b1f54ce8a534a3f1e774a30e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3eb8b9bc0dbb4b6c97972503006e931c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "748e9871eeea4ad581e135047d750c6c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3b8a56361dc84702a60598d532349317", "style": "IPY_MODEL_2a00fc4e632047bcbfbf5529ada41dfe", "value": "<p>Calculate the calibration curve with brier score for each class. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_calibration_score.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3b8a56361dc84702a60598d532349317": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a00fc4e632047bcbfbf5529ada41dfe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1a206f6c5cc5410a9162ed4202a4889e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_00f4388a3d0040bc94f3a9aec46b64af", "IPY_MODEL_cc49415a4be04ad49fac83955ee90aec", "IPY_MODEL_1774629963c9432397386043b9595117", "IPY_MODEL_257ff93e057249d8a4eabc4f5bdf13bf", "IPY_MODEL_aa9fad9ac77341498fe1a10f377b0668"], "layout": "IPY_MODEL_98d758311061425f9db56ba9070700e1"}}, "00f4388a3d0040bc94f3a9aec46b64af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e7a211a44c8d450eb1993bbb5bc0a9ec", "style": "IPY_MODEL_4802a9810bbe47b387c581812cb98a44", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e7a211a44c8d450eb1993bbb5bc0a9ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4802a9810bbe47b387c581812cb98a44": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cc49415a4be04ad49fac83955ee90aec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e1ed5c068664915a5aaca8ed03ba47d", "style": "IPY_MODEL_7ec1218453974ce1a8d3a7faac4d4a1a", "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>"}}, "3e1ed5c068664915a5aaca8ed03ba47d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ec1218453974ce1a8d3a7faac4d4a1a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1774629963c9432397386043b9595117": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"line": {"dash": "dash", "width": 2}, "name": "Perfectly calibrated", "x": [0, 1], "y": [0, 1], "type": "scatter", "uid": "aa5e4854-7998-4352-b38d-a94c1dd00d0c"}, {"mode": "lines+markers", "name": "(brier:   0.0070)", "x": {"dtype": "float64", "shape": [4]}, "y": {"dtype": "float64", "shape": [4]}, "type": "scatter", "uid": "6031b1f9-cfb2-48dc-a497-1d01cb62e6b4"}], "_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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"_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": "EEKWgSCyED8sDqmw8JPCP7OAcALfns4/GOFwMvT/7z8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "gDfRMNiYcj8AAAAAAAAAAAAAAAAAAAAAVDqyZyAq7T8="}]}, "257ff93e057249d8a4eabc4f5bdf13bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_55edb6f9b636415794389dcf9bf5357d", "style": "IPY_MODEL_8a93bb770fea43adb80f4e8132e1dc91", "value": "<div>The Brier score metric may be used to assess how well a classifier is calibrated. 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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:** ( 2 minutes  28.396 seconds)


.. _sphx_glr_download_user-guide_tabular_auto_tutorials_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>`_