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

.. only:: html

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

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

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

.. _sphx_glr_tabular_auto_tutorials_quickstarts_plot_quick_train_test_validation.py:


.. _quick_train_test_validation:

Train-Test Validation Suite Quickstart
****************************************

The deepchecks train-test validation suite is relevant any time you wish to 
validate two data subsets. For example:

- Comparing distributions across different train-test splits (e.g. before 
  training a model or when splitting data for cross-validation)
- Comparing a new data batch to previous data batches

Here we'll use a loans' dataset
(:mod:`deepchecks.tabular.datasets.classification.lending_club`),
to demonstrate how you can run the suite with only a few simple lines of code, 
and see which kind of insights it can find.

.. code-block:: bash

    # Before we start, if you don't have deepchecks installed yet, run:
    import sys
    !{sys.executable} -m pip install deepchecks -U --quiet

    # or install using pip from your python environment

.. GENERATED FROM PYTHON SOURCE LINES 30-35

Load Data and Prepare Data
====================================================

Load Data
-----------

.. GENERATED FROM PYTHON SOURCE LINES 35-45

.. code-block:: default



    from deepchecks.tabular.datasets.classification import lending_club
    import pandas as pd

    data = lending_club.load_data(data_format='Dataframe', as_train_test=False)
    data.head(2)








.. 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>issue_d</th>
          <th>sub_grade</th>
          <th>term</th>
          <th>home_ownership</th>
          <th>fico_range_low</th>
          <th>total_acc</th>
          <th>pub_rec</th>
          <th>revol_util</th>
          <th>annual_inc</th>
          <th>int_rate</th>
          <th>dti</th>
          <th>purpose</th>
          <th>mort_acc</th>
          <th>loan_amnt</th>
          <th>application_type</th>
          <th>installment</th>
          <th>verification_status</th>
          <th>pub_rec_bankruptcies</th>
          <th>addr_state</th>
          <th>initial_list_status</th>
          <th>fico_range_high</th>
          <th>revol_bal</th>
          <th>id</th>
          <th>open_acc</th>
          <th>emp_length</th>
          <th>loan_status</th>
          <th>time_to_earliest_cr_line</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>2017-06-01</td>
          <td>D1</td>
          <td>36 months</td>
          <td>MORTGAGE</td>
          <td>665.0</td>
          <td>29.0</td>
          <td>0.0</td>
          <td>85.0</td>
          <td>112600.0</td>
          <td>17.09</td>
          <td>22.71</td>
          <td>debt_consolidation</td>
          <td>4.0</td>
          <td>24000.0</td>
          <td>Individual</td>
          <td>856.75</td>
          <td>Verified</td>
          <td>0.0</td>
          <td>CO</td>
          <td>w</td>
          <td>669.0</td>
          <td>25779.0</td>
          <td>110680237</td>
          <td>13.0</td>
          <td>2.0</td>
          <td>0</td>
          <td>794188.8</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2017-06-01</td>
          <td>C2</td>
          <td>36 months</td>
          <td>RENT</td>
          <td>670.0</td>
          <td>14.0</td>
          <td>0.0</td>
          <td>34.8</td>
          <td>35000.0</td>
          <td>13.59</td>
          <td>9.95</td>
          <td>debt_consolidation</td>
          <td>1.0</td>
          <td>4200.0</td>
          <td>Individual</td>
          <td>142.72</td>
          <td>Source Verified</td>
          <td>NaN</td>
          <td>FL</td>
          <td>f</td>
          <td>674.0</td>
          <td>3798.0</td>
          <td>109936186</td>
          <td>7.0</td>
          <td>2.0</td>
          <td>1</td>
          <td>470793.6</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-48

Split Data to Train and Test
-----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 48-57

.. code-block:: default


    # convert date column to datetime, `issue_d`` is date column
    data['issue_d'] = pd.to_datetime(data['issue_d'])

    # Use data from June and July for train and August for test:
    train_df = data[data['issue_d'].dt.month.isin([6, 7])]
    test_df = data[data['issue_d'].dt.month.isin([8])]









.. GENERATED FROM PYTHON SOURCE LINES 58-67

Run Deepchecks for Train Test Validation
===========================================

Define a Dataset Object
-------------------------

Create a deepchecks Dataset, including the relevant metadata (label, date, index, etc.).
Check out :class:`deepchecks.tabular.Dataset` to see all of the columns and types 
that can be declared.

.. GENERATED FROM PYTHON SOURCE LINES 70-72

Define Lending Club Metadata
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 72-80

.. code-block:: default


    categorical_features = ['addr_state', 'application_type', 'home_ownership', \
      'initial_list_status', 'purpose', 'term', 'verification_status', 'sub_grade']
    index_name = 'id'
    label = 'loan_status' # 0 is DEFAULT, 1 is OK
    datetime_name = 'issue_d'









.. GENERATED FROM PYTHON SOURCE LINES 81-83

Create Dataset
^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 83-96

.. code-block:: default


    from deepchecks.tabular import Dataset

    # Categorical features can be heuristically inferred, however we
    # recommend to state them explicitly to avoid misclassification.

    # Metadata attributes are optional. Some checks will run only if specific attributes are declared.

    train_ds = Dataset(train_df, label=label,cat_features=categorical_features, \
                       index_name=index_name, datetime_name=datetime_name)
    test_ds = Dataset(test_df, label=label,cat_features=categorical_features, \
                       index_name=index_name, datetime_name=datetime_name)








.. GENERATED FROM PYTHON SOURCE LINES 97-102

.. code-block:: default


    # for convenience lets save it in a dictionary so we can reuse them for future Dataset initializations
    columns_metadata = {'cat_features' : categorical_features, 'index_name': index_name,
                        'label':label, 'datetime_name':datetime_name}








.. GENERATED FROM PYTHON SOURCE LINES 103-112

Run the Deepchecks Suite
--------------------------

Validate your data with the :class:`deepchecks.tabular.suites.train_test_validation` suite.
It runs on two datasets, so you can use it to compare any two batches of data (e.g. train data, test data, a new batch of data
that recently arrived)

Check out the :ref:`when you should use <when_should_you_use_deepchecks>`
for some more info about the existing suites and when to use them.

.. GENERATED FROM PYTHON SOURCE LINES 112-121

.. code-block:: default


    from deepchecks.tabular.suites import train_test_validation

    validation_suite = train_test_validation()
    suite_result = validation_suite.run(train_ds, test_ds)
    # Note: the result can be saved as html using suite_result.save_as_html()
    # or exported to json using suite_result.to_json()
    suite_result





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

 .. code-block:: none


    Train Test Validation Suite:
    |            | 0/12 [Time: 00:00]
    Train Test Validation Suite:
    |████        | 4/12 [Time: 00:00, Check=String Mismatch Comparison]
    Train Test Validation Suite:
    |███████     | 7/12 [Time: 00:00, Check=Index Train Test Leakage]  
    Train Test Validation Suite:
    |██████████  | 10/12 [Time: 00:05, Check=Feature Drift]          
    Train Test Validation Suite:
    |████████████| 12/12 [Time: 00:07, Check=Multivariate Drift]
                                                                                         

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        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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text-align: left;\n  white-space: pre-wrap;\n}\n#T_5b899_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5b899_\">\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_5b899_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_5b899_row0_col1\" class=\"data row0 col1\" ><a href=\"#DateTrain-TestLeakage(overlap)_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-VKEKX\">Date Train-Test Leakage (overlap)</a></td>\n      <td id=\"T_5b899_row0_col2\" class=\"data row0 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_5b899_row0_col3\" class=\"data row0 col3\" >Found 78.21% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "260ac11948a943a39b1e64ae08347809": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "56175cb365504243979dfa3c5fb5c7f0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c70942c61f534c26acf3197222c7cb4b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c2713a794b34e098ef8a3374c88dd50", "style": "IPY_MODEL_57c3b3700f574c5bbca1549d9199d6c7", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "4c2713a794b34e098ef8a3374c88dd50": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57c3b3700f574c5bbca1549d9199d6c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c3bfe3340ca94327adfdf4e579478fd5": {"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_fa5b3fa555414146b4511bd1da11848e", "IPY_MODEL_ca458d363267470eae2865a68f7ce096", "IPY_MODEL_f51057f281a84dfa89ddc1794c0ccd67", "IPY_MODEL_41ba6625ccd04f2b8c9e6f8e2d1af345"], "layout": "IPY_MODEL_6363be730294412397837123e5419541"}}, "fa5b3fa555414146b4511bd1da11848e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_22246749a4e54caa92739892c00720b6", "style": "IPY_MODEL_4b5ebe6dd8bd49ec9a0ef1a0252e0e4b", "value": "<h4 id=\"DateTrain-TestLeakage(overlap)_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-VKEKX\"><b>Date Train-Test Leakage (overlap)</b></h4>"}}, "22246749a4e54caa92739892c00720b6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b5ebe6dd8bd49ec9a0ef1a0252e0e4b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ca458d363267470eae2865a68f7ce096": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c669e62df8d340e8a25ceaa6e8922e29", "style": "IPY_MODEL_abeb360bd2b74e028fc22dc7a6323799", "value": "<p>Check test data that is dated earlier than the latest date in train. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_date_train_test_leakage_overlap.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c669e62df8d340e8a25ceaa6e8922e29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "abeb360bd2b74e028fc22dc7a6323799": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f51057f281a84dfa89ddc1794c0ccd67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_50317df03b6b49059ef6c118160b2f1c", "style": "IPY_MODEL_9d68260ce632400fab7a97fcd0e2ecc9", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_7ca71_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ca71_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ca71_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ca71_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7ca71_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7ca71_\">\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_7ca71_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_7ca71_row0_col1\" class=\"data row0 col1\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_7ca71_row0_col2\" class=\"data row0 col2\" >Found 78.21% leaked dates</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "50317df03b6b49059ef6c118160b2f1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d68260ce632400fab7a97fcd0e2ecc9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "41ba6625ccd04f2b8c9e6f8e2d1af345": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_050bdc0ebbf44cca81a209a6e9d002c7", "IPY_MODEL_c5fda7e92db4457caeeafeea4da95344", "IPY_MODEL_96e31bfb9c8644b6b917bbf5dcc9f6d6"], "layout": "IPY_MODEL_f221fcefa4a44c68b0e67a3fc5217172"}}, "050bdc0ebbf44cca81a209a6e9d002c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53432cfd96c9416c9990d321db0db538", "style": "IPY_MODEL_da7dc8b2cf404026967e7be7597b21d9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "53432cfd96c9416c9990d321db0db538": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da7dc8b2cf404026967e7be7597b21d9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c5fda7e92db4457caeeafeea4da95344": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bba6416f60994fc98c56a3fb6e6a9a18", "style": "IPY_MODEL_51d4f09fca9c4dda933173af7b94fac3", "value": "<div>78.21% of test data samples are in the date range 2017-08-01 - 2018-07-01, which occurs before last training data date (2018-07-01)</div>"}}, "bba6416f60994fc98c56a3fb6e6a9a18": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51d4f09fca9c4dda933173af7b94fac3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96e31bfb9c8644b6b917bbf5dcc9f6d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2ad6e933cfd4494388f6eebef1c6f37c", "style": "IPY_MODEL_d461bb7a6caa4992b556253bc38963fb", "value": "<br><a href=\"#summary_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-VKEKX\" style=\"font-size: 14px;\">Go to top</a>"}}, "2ad6e933cfd4494388f6eebef1c6f37c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d461bb7a6caa4992b556253bc38963fb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f221fcefa4a44c68b0e67a3fc5217172": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6363be730294412397837123e5419541": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5fd26dad0c5a4a928007db146bce8896": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09faf04c88ae487990230b4857725988": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db0adc17929c45ea97bfb3198170a64a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8a84d8edd984d4eb4ee200b71f69af9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f61f4ccf5ef3437583266d9f171675ba", "IPY_MODEL_51db7fe06511484a8d5230d6cb77869a"], "layout": "IPY_MODEL_ea03fcaadd734e53b0105d8a72de643d"}}, "f61f4ccf5ef3437583266d9f171675ba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8462a5676f4e425e9ba82c382d83baae", "style": "IPY_MODEL_61bb6c7bcd644099b267c489635f50e6", "value": "<span id=\"summary_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\"></span>"}}, "8462a5676f4e425e9ba82c382d83baae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61bb6c7bcd644099b267c489635f50e6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "51db7fe06511484a8d5230d6cb77869a": {"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_f5b4e81f569f43b09ee8c2a5748e6b87"], "layout": "IPY_MODEL_3a1ba81f54c74f95b3d0f240c0a1f8a1", "selected_index": null}}, "f5b4e81f569f43b09ee8c2a5748e6b87": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_98e4b639f6b84dc7855661493fca8324", "IPY_MODEL_de967b6b041f4e2f84b45f93a2f10448", "IPY_MODEL_ff4e73df021248a383c0431aeab35d3a", "IPY_MODEL_08d73cb1973f4840b009203d7f5b4225", "IPY_MODEL_4f9480c35bae40e99e2f8d5665df5224", "IPY_MODEL_08d73cb1973f4840b009203d7f5b4225", "IPY_MODEL_ceef7681213c453891c9f5c4d7cf1d5f", "IPY_MODEL_08d73cb1973f4840b009203d7f5b4225", "IPY_MODEL_71f2116b3aed46a6a929033d684e419d", "IPY_MODEL_08d73cb1973f4840b009203d7f5b4225", "IPY_MODEL_759745995d2a462bb74911ffbc7f4e97", "IPY_MODEL_08d73cb1973f4840b009203d7f5b4225", "IPY_MODEL_a8af95e27a9949f3a8d4403833a8f94a"], "layout": "IPY_MODEL_7a62044ee03c485f82c1197e18d1b80a"}}, "98e4b639f6b84dc7855661493fca8324": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b0c743d4c61c431986229b148ea35888", "style": "IPY_MODEL_edc2532cf1cb48e2ac1beb659fb39d17", "value": "<style type=\"text/css\">\n#T_57825_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57825_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57825_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57825_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_57825_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_57825_\">\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_57825_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">Datasets Size Comparison</a></td>\n      <td id=\"T_57825_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_57825_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.49</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row1_col1\" class=\"data row1 col1\" ><a href=\"#NewCategoryTrainTest_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">New Category Train Test</a></td>\n      <td id=\"T_57825_row1_col2\" class=\"data row1 col2\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_57825_row1_col3\" class=\"data row1 col3\" >Passed for 8 relevant features</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row2_col1\" class=\"data row2 col1\" ><a href=\"#FeatureLabelCorrelationChange_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">Feature Label Correlation Change</a></td>\n      <td id=\"T_57825_row2_col2\" class=\"data row2 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_57825_row2_col3\" class=\"data row2 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row3_col1\" class=\"data row3 col1\" ><a href=\"#FeatureLabelCorrelationChange_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">Feature Label Correlation Change</a></td>\n      <td id=\"T_57825_row3_col2\" class=\"data row3 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_57825_row3_col3\" class=\"data row3 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row4_col1\" class=\"data row4 col1\" ><a href=\"#MultivariateDrift_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">Multivariate Drift</a></td>\n      <td id=\"T_57825_row4_col2\" class=\"data row4 col2\" >Drift value is less than 0.25</td>\n      <td id=\"T_57825_row4_col3\" class=\"data row4 col3\" >Found drift value of: 0.04, corresponding to a domain classifier AUC of: 0.52</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row5_col1\" class=\"data row5 col1\" ><a href=\"#LabelDrift_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">Label Drift</a></td>\n      <td id=\"T_57825_row5_col2\" class=\"data row5 col2\" >Label drift score < 0.15</td>\n      <td id=\"T_57825_row5_col3\" class=\"data row5 col3\" >Label's drift score Cramer's V is 5.47E-3</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row6_col1\" class=\"data row6 col1\" ><a href=\"#FeatureDrift_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\">Feature Drift</a></td>\n      <td id=\"T_57825_row6_col2\" class=\"data row6 col2\" >categorical drift score < 0.2 and numerical drift score < 0.2</td>\n      <td id=\"T_57825_row6_col3\" class=\"data row6 col3\" >Passed for 24 columns out of 24 columns.\nFound column \"application_type\" has the highest categorical drift score: 0.04\nFound column \"emp_length\" has the highest numerical drift score: 0.04</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row7_col1\" class=\"data row7 col1\" >New Label Train Test</td>\n      <td id=\"T_57825_row7_col2\" class=\"data row7 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_57825_row7_col3\" class=\"data row7 col3\" >Found 0 new labels in test data: []</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row8_col1\" class=\"data row8 col1\" >Train Test Samples Mix</td>\n      <td id=\"T_57825_row8_col2\" class=\"data row8 col2\" >Percentage of test data samples that appear in train data is less or equal to 5%</td>\n      <td id=\"T_57825_row8_col3\" class=\"data row8 col3\" >No samples mix found</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row9_col1\" class=\"data row9 col1\" >Index Train-Test Leakage</td>\n      <td id=\"T_57825_row9_col2\" class=\"data row9 col2\" >Ratio of leaking indices is less or equal to 0%</td>\n      <td id=\"T_57825_row9_col3\" class=\"data row9 col3\" >No index leakage found</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row10_col0\" class=\"data row10 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row10_col1\" class=\"data row10 col1\" >Date Train-Test Leakage (duplicates)</td>\n      <td id=\"T_57825_row10_col2\" class=\"data row10 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_57825_row10_col3\" class=\"data row10 col3\" >No leaked dates found</td>\n    </tr>\n    <tr>\n      <td id=\"T_57825_row11_col0\" class=\"data row11 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_57825_row11_col1\" class=\"data row11 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_57825_row11_col2\" class=\"data row11 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_57825_row11_col3\" class=\"data row11 col3\" >Passed for 8 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b0c743d4c61c431986229b148ea35888": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edc2532cf1cb48e2ac1beb659fb39d17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "de967b6b041f4e2f84b45f93a2f10448": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0f6c27a005ff4c6f8b6eb88077fe1a3e", "style": "IPY_MODEL_9a0276898be343ae94f8e91b64090ea7", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "0f6c27a005ff4c6f8b6eb88077fe1a3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a0276898be343ae94f8e91b64090ea7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ff4e73df021248a383c0431aeab35d3a": {"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_8da637cc7edd492b8d99fa9f061ccd7b", "IPY_MODEL_6e131d4341284e3aa5db82892d6102e4", "IPY_MODEL_1fef5523adb7486f979d956722255113", "IPY_MODEL_7b964c2f2d8e4f7cab88f9b684445cb0"], "layout": "IPY_MODEL_729b9797dc5446dfaf933d3de205e839"}}, "8da637cc7edd492b8d99fa9f061ccd7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eb84bd47d84f41aaa65032ef78353946", "style": "IPY_MODEL_ecf568d6a2654c0b9fa6c3859064e039", "value": "<h4 id=\"DatasetsSizeComparison_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\"><b>Datasets Size Comparison</b></h4>"}}, "eb84bd47d84f41aaa65032ef78353946": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ecf568d6a2654c0b9fa6c3859064e039": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6e131d4341284e3aa5db82892d6102e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a33c4cc90694901b3ce77efdb94fd70", "style": "IPY_MODEL_69a8a5b0ee67412791d5ff3947fd2e79", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_datasets_size_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "8a33c4cc90694901b3ce77efdb94fd70": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69a8a5b0ee67412791d5ff3947fd2e79": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1fef5523adb7486f979d956722255113": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f32c2b26a2f5409aad8132fa9c23ab93", "style": "IPY_MODEL_3eed8252de594483a2254f7e85e92009", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b334c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b334c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b334c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b334c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b334c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b334c_\">\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_b334c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_b334c_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_b334c_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.49</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f32c2b26a2f5409aad8132fa9c23ab93": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3eed8252de594483a2254f7e85e92009": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7b964c2f2d8e4f7cab88f9b684445cb0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_697ec8dc944b4f439fdefb77eb1e28f1", "IPY_MODEL_605bbfef116b48e899fa4e655a6515e0", "IPY_MODEL_5409007e590a4a9eb25dc4e3417a353a"], "layout": "IPY_MODEL_903fb98044fb4bdf83c69634b8383712"}}, "697ec8dc944b4f439fdefb77eb1e28f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_00cc191439c1424f83925bffda1a0dc6", "style": "IPY_MODEL_46eaa073b7a64ef6840c4960d19c5374", "value": "<h5><b>Additional Outputs</b></h5>"}}, "00cc191439c1424f83925bffda1a0dc6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "46eaa073b7a64ef6840c4960d19c5374": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "605bbfef116b48e899fa4e655a6515e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5fb5dadf594b47e3ab10bf6572addbdb", "style": "IPY_MODEL_4c4669b8125d4a808a424c19d7b3ad53", "value": "<style type=\"text/css\">\n#T_65903_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_65903_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_65903_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_65903_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_65903_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_65903_\">\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_65903_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_65903_row0_col0\" class=\"data row0 col0\" >14556</td>\n      <td id=\"T_65903_row0_col1\" class=\"data row0 col1\" >7112</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5fb5dadf594b47e3ab10bf6572addbdb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c4669b8125d4a808a424c19d7b3ad53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5409007e590a4a9eb25dc4e3417a353a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5cf3121f0fe24453a6833a5d62954657", "style": "IPY_MODEL_43c7d73e4e8c42a9a5eab7ae929be600", "value": "<br><a href=\"#summary_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\" style=\"font-size: 14px;\">Go to top</a>"}}, "5cf3121f0fe24453a6833a5d62954657": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43c7d73e4e8c42a9a5eab7ae929be600": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "903fb98044fb4bdf83c69634b8383712": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "729b9797dc5446dfaf933d3de205e839": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "08d73cb1973f4840b009203d7f5b4225": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b77f118b51af4b019738b17e5dbde1fc", "style": "IPY_MODEL_00b6208d2bc44656aed5a8d5cf4917d2", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b77f118b51af4b019738b17e5dbde1fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "00b6208d2bc44656aed5a8d5cf4917d2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f9480c35bae40e99e2f8d5665df5224": {"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_5d75aa82cac74ef2b99cab6fc83130b9", "IPY_MODEL_71b9f7bb81cb496281df8f8865479e40", "IPY_MODEL_7bdda9fc24004b9c81e81c1365087aa8", "IPY_MODEL_bf22c810e10f4419a9cac62d9e9a8178"], "layout": "IPY_MODEL_20b8dd0e40b54abb9440fc7998a01007"}}, "5d75aa82cac74ef2b99cab6fc83130b9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_262e2d4ab2244d6aa89056d61c2d5c83", "style": "IPY_MODEL_4fb607bbe5074a2b8de1687b383ad38c", "value": "<h4 id=\"NewCategoryTrainTest_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\"><b>New Category Train Test</b></h4>"}}, "262e2d4ab2244d6aa89056d61c2d5c83": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4fb607bbe5074a2b8de1687b383ad38c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "71b9f7bb81cb496281df8f8865479e40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47817964f2fa49d5b7f4eed64c96eb41", "style": "IPY_MODEL_fa172ff9e075460cb609c48ab6132902", "value": "<p>Find new categories in the test set. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_new_category_train_test.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "47817964f2fa49d5b7f4eed64c96eb41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa172ff9e075460cb609c48ab6132902": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7bdda9fc24004b9c81e81c1365087aa8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bc7440e7a6d844efb415ab624c6957f3", "style": "IPY_MODEL_cc9ddd7cd9ce4eee8e255f3b3d713aa2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d776c_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d776c_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d776c_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d776c_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d776c_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d776c_\">\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_d776c_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d776c_row0_col1\" class=\"data row0 col1\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_d776c_row0_col2\" class=\"data row0 col2\" >Passed for 8 relevant features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bc7440e7a6d844efb415ab624c6957f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc9ddd7cd9ce4eee8e255f3b3d713aa2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf22c810e10f4419a9cac62d9e9a8178": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_660196d646dd418ab124887050e0933f", "IPY_MODEL_2f6cc9b335b644129290135e594e8d09", "IPY_MODEL_c223cb93a1004414b1c30cb7694f7dec"], "layout": "IPY_MODEL_49fb7943bb3b4df7b31ac527a5e8107e"}}, "660196d646dd418ab124887050e0933f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b542154eeb4d496facbdca8760aa9bc8", "style": "IPY_MODEL_6dac3bbc906046aaa5689e7383a98fd8", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b542154eeb4d496facbdca8760aa9bc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6dac3bbc906046aaa5689e7383a98fd8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2f6cc9b335b644129290135e594e8d09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f7f66218582a4aec87a201adefd4ac44", "style": "IPY_MODEL_70f38462b81248a0be29b33116db5728", "value": "<style type=\"text/css\">\n#T_b5f40_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b5f40_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b5f40_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b5f40_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b5f40_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b5f40_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" ># New Categories</th>\n      <th class=\"col_heading level0 col1\" >Ratio of New Categories</th>\n      <th class=\"col_heading level0 col2\" >New Categories Names</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Feature Name</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b5f40_level0_row0\" class=\"row_heading level0 row0\" >purpose</th>\n      <td id=\"T_b5f40_row0_col0\" class=\"data row0 col0\" >0</td>\n      <td id=\"T_b5f40_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_b5f40_row0_col2\" class=\"data row0 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_b5f40_level0_row1\" class=\"row_heading level0 row1\" >application_type</th>\n      <td id=\"T_b5f40_row1_col0\" class=\"data row1 col0\" >0</td>\n      <td id=\"T_b5f40_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_b5f40_row1_col2\" class=\"data row1 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_b5f40_level0_row2\" class=\"row_heading level0 row2\" >home_ownership</th>\n      <td id=\"T_b5f40_row2_col0\" class=\"data row2 col0\" >0</td>\n      <td id=\"T_b5f40_row2_col1\" class=\"data row2 col1\" >0%</td>\n      <td id=\"T_b5f40_row2_col2\" class=\"data row2 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_b5f40_level0_row3\" class=\"row_heading level0 row3\" >verification_status</th>\n      <td id=\"T_b5f40_row3_col0\" class=\"data row3 col0\" >0</td>\n      <td id=\"T_b5f40_row3_col1\" class=\"data row3 col1\" >0%</td>\n      <td id=\"T_b5f40_row3_col2\" class=\"data row3 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_b5f40_level0_row4\" class=\"row_heading level0 row4\" >term</th>\n      <td id=\"T_b5f40_row4_col0\" class=\"data row4 col0\" >0</td>\n      <td id=\"T_b5f40_row4_col1\" class=\"data row4 col1\" >0%</td>\n      <td id=\"T_b5f40_row4_col2\" class=\"data row4 col2\" >[]</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f7f66218582a4aec87a201adefd4ac44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "70f38462b81248a0be29b33116db5728": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c223cb93a1004414b1c30cb7694f7dec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2939e1725be645f9971b449aab357941", "style": "IPY_MODEL_1804258b48d04de5aa77fc16ee273176", "value": "<br><a href=\"#summary_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\" style=\"font-size: 14px;\">Go to top</a>"}}, "2939e1725be645f9971b449aab357941": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1804258b48d04de5aa77fc16ee273176": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49fb7943bb3b4df7b31ac527a5e8107e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "20b8dd0e40b54abb9440fc7998a01007": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ceef7681213c453891c9f5c4d7cf1d5f": {"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_eda661316c064ac8b5142fe0f503b428", "IPY_MODEL_ca47c4466b04485f97075e2f124e59d0", "IPY_MODEL_39f5a15b478a4a44b06228fb63f7a743", "IPY_MODEL_02cebe74840f43c6ba2047c36ad1907f"], "layout": "IPY_MODEL_9776291ecb3c411692e2468afe4f3edc"}}, "eda661316c064ac8b5142fe0f503b428": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a68bf11b4c6e41d3995f50872977ee0c", "style": "IPY_MODEL_b0b16e323c42467f9ba5d279089bef06", "value": "<h4 id=\"FeatureLabelCorrelationChange_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\"><b>Feature Label Correlation Change</b></h4>"}}, "a68bf11b4c6e41d3995f50872977ee0c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b0b16e323c42467f9ba5d279089bef06": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ca47c4466b04485f97075e2f124e59d0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_56bfd49955ec4af881679922fcd6f249", "style": "IPY_MODEL_c57a27a495d54e7387ccdb3eb16b7217", "value": "<p>    Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "56bfd49955ec4af881679922fcd6f249": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c57a27a495d54e7387ccdb3eb16b7217": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "39f5a15b478a4a44b06228fb63f7a743": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c72a4f3df934e169fa180d06d81019f", "style": "IPY_MODEL_970ee4053945471aac6aac662c9f45bf", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_0f744_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0f744_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0f744_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0f744_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0f744_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0f744_\">\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_0f744_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_0f744_row0_col1\" class=\"data row0 col1\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_0f744_row0_col2\" class=\"data row0 col2\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_0f744_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_0f744_row1_col1\" class=\"data row1 col1\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_0f744_row1_col2\" class=\"data row1 col2\" >Passed for 24 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5c72a4f3df934e169fa180d06d81019f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "970ee4053945471aac6aac662c9f45bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02cebe74840f43c6ba2047c36ad1907f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_269be559e76f4439b6609a9efdda31cf", "IPY_MODEL_79c1b7683cde4d4ea5a74d53e65715c6", "IPY_MODEL_4e3d102f0d6d4c6392000ba57aa3426a", "IPY_MODEL_12acf79713c645ed9bfb663e3c2b2057", "IPY_MODEL_4ca5de129d6f472382a8a344ab05d8c0", "IPY_MODEL_003fe0ac6665488fa68e6709a6af378e", "IPY_MODEL_b72ad666803a4a138599031056650276", "IPY_MODEL_f871cc96d4ce44a9a1d750a7b02f13a0", "IPY_MODEL_eed33bd222e04755afa3612b96ab6c70", "IPY_MODEL_0f99e8f983444ee0b54dd1d88b0a6811", "IPY_MODEL_acf04578ab4644518e719956a0af47a8", "IPY_MODEL_ccb1fc7096c244668bda8eccddfb649a", "IPY_MODEL_436976e8545745f0a546d162e36bfba8"], "layout": "IPY_MODEL_40e17b502f2d483d89cd273468fd675e"}}, "269be559e76f4439b6609a9efdda31cf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_52f56621947945c591c4770075715fa8", "style": "IPY_MODEL_33e68e0274744710a3c57c999abbad4d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "52f56621947945c591c4770075715fa8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33e68e0274744710a3c57c999abbad4d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "79c1b7683cde4d4ea5a74d53e65715c6": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.1</b>", "<b>0.02</b>", "<b>0.01</b>", "<b>0.03</b>", "<b>0.05</b>"], "textposition": "outside", "x": ["int_rate", "purpose", "revol_util", "time_to_earliest_cr_line", "installment"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "6884d1ff-3c87-4f48-be66-f0b9a5c80333"}, {"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>0.07</b>", "<b>0.0</b>", "<b>0.0</b>", "<b>0.02</b>", "<b>0.04</b>"], "textposition": "outside", "x": ["int_rate", "purpose", "revol_util", "time_to_earliest_cr_line", "installment"], "y": {"dtype": "float64", "shape": [5]}, "type": "bar", "uid": "8b481a77-8912-4ca3-bb2a-e92edcb42874"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"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": "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": "uFCsuWAIuT9hKjr3kseWP/c4Vtehe4g/hNCE9AUJoD9VXGeRMiyoPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "sWLev1f8sD8AAAAAAAAAAAAAAAAAAAAAg4nJ3CCLlD992tkqIMGjPw=="}]}, "4e3d102f0d6d4c6392000ba57aa3426a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c1d8adebd9154b8eb425840724d1a3d7", "style": "IPY_MODEL_dfc7a74a445240d39b6d5d00489ebf58", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "c1d8adebd9154b8eb425840724d1a3d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dfc7a74a445240d39b6d5d00489ebf58": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12acf79713c645ed9bfb663e3c2b2057": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4d3abee167c1439eba501395d6274994", "style": "IPY_MODEL_39a7c39abd6e45f7b69aa1fe31bbb3db", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "4d3abee167c1439eba501395d6274994": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39a7c39abd6e45f7b69aa1fe31bbb3db": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4ca5de129d6f472382a8a344ab05d8c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21becbc263e840ba85982f1d556c787a", "style": "IPY_MODEL_83a5d49a90e74eeeaac85575012bd0d1", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "21becbc263e840ba85982f1d556c787a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "83a5d49a90e74eeeaac85575012bd0d1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "003fe0ac6665488fa68e6709a6af378e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_88137e27dd9342dd861d683916913fdb", "style": "IPY_MODEL_4492f009ed2b4b3a892be574e4ae11e5", "value": "<div>Can indicate that this feature's success in predicting the label is actually due to data leakage, </div>"}}, "88137e27dd9342dd861d683916913fdb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4492f009ed2b4b3a892be574e4ae11e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b72ad666803a4a138599031056650276": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_abf5a2d499ee4f1da06aa21799fd75ec", "style": "IPY_MODEL_9e8fc4474626434ba1cb6cbe1610709a", "value": "<div>   meaning that the feature holds information that is based on the label to begin with.</div>"}}, "abf5a2d499ee4f1da06aa21799fd75ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9e8fc4474626434ba1cb6cbe1610709a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f871cc96d4ce44a9a1d750a7b02f13a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a96e4d504d4433290025a72d2e8687a", "style": "IPY_MODEL_19420d80f430458986794aac47efe21d", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "3a96e4d504d4433290025a72d2e8687a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "19420d80f430458986794aac47efe21d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eed33bd222e04755afa3612b96ab6c70": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cafcf9935b1d4343a34dcffb0b88cf56", "style": "IPY_MODEL_3cf6b263d1b04d3ba27cfac476726cf0", "value": "<div>   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test </div>"}}, "cafcf9935b1d4343a34dcffb0b88cf56": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3cf6b263d1b04d3ba27cfac476726cf0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f99e8f983444ee0b54dd1d88b0a6811": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8596bcdbdfb448e393916bceda108ead", "style": "IPY_MODEL_6ceec65a72e343ffb1c2ff77929cb605", "value": "<div>   can be explained by leakage in train that is not relevant to a new dataset.</div>"}}, "8596bcdbdfb448e393916bceda108ead": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ceec65a72e343ffb1c2ff77929cb605": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "acf04578ab4644518e719956a0af47a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c41b4d352d3940a59cceb3b456f39427", "style": "IPY_MODEL_3dbe7f97011f4fb8b62009b5d53a50d7", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "c41b4d352d3940a59cceb3b456f39427": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3dbe7f97011f4fb8b62009b5d53a50d7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ccb1fc7096c244668bda8eccddfb649a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e3191da793cd4495a8a57653be2e95ed", "style": "IPY_MODEL_62c9e568e53a4cab8e6ab910d8e4f2b3", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "e3191da793cd4495a8a57653be2e95ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "62c9e568e53a4cab8e6ab910d8e4f2b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "436976e8545745f0a546d162e36bfba8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6bd9e19784054eb785e3383770bf73de", "style": "IPY_MODEL_c39e677314fa44c9b3b0ed22fbf22c25", "value": "<br><a href=\"#summary_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\" style=\"font-size: 14px;\">Go to top</a>"}}, "6bd9e19784054eb785e3383770bf73de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c39e677314fa44c9b3b0ed22fbf22c25": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40e17b502f2d483d89cd273468fd675e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9776291ecb3c411692e2468afe4f3edc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71f2116b3aed46a6a929033d684e419d": {"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_7a05f5ff48834a73b0a03268d26bc7d5", "IPY_MODEL_ffc3442b0f2548eba7983fb16221f836", "IPY_MODEL_7f27657d51d7407db3af69df736c7989", "IPY_MODEL_3601fab155254ae9b31f35dd0ab22140"], "layout": "IPY_MODEL_37ced373231945a6965b1bdd90852c53"}}, "7a05f5ff48834a73b0a03268d26bc7d5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51fce2c1f6974f48bb67dbc373ea9955", "style": "IPY_MODEL_db49b647fc904f8aa57cbbce445015ff", "value": "<h4 id=\"FeatureDrift_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\"><b>Feature Drift</b></h4>"}}, "51fce2c1f6974f48bb67dbc373ea9955": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db49b647fc904f8aa57cbbce445015ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ffc3442b0f2548eba7983fb16221f836": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9fd77acb821141a08fb8ad85169bb941", "style": "IPY_MODEL_49a268b9cb124f249c79caeda409520b", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9fd77acb821141a08fb8ad85169bb941": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "49a268b9cb124f249c79caeda409520b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f27657d51d7407db3af69df736c7989": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6958af84d114981906d071a8fc75727", "style": "IPY_MODEL_7508c03d37ce4edeb53b0096a0ecfeeb", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5a0b5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5a0b5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5a0b5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5a0b5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5a0b5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5a0b5_\">\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_5a0b5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5a0b5_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.2</td>\n      <td id=\"T_5a0b5_row0_col2\" class=\"data row0 col2\" >Passed for 24 columns out of 24 columns.\nFound column \"application_type\" has the highest categorical drift score: 0.04\nFound column \"emp_length\" has the highest numerical drift score: 0.04</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e6958af84d114981906d071a8fc75727": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7508c03d37ce4edeb53b0096a0ecfeeb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3601fab155254ae9b31f35dd0ab22140": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d4411c9e62d547f886f3cd7e05642edd", "IPY_MODEL_d3157a4b10db420ab18a48fa1ae9d8a7", "IPY_MODEL_b6b84879583e4b0f904d88b7c2ddb340", "IPY_MODEL_2fb28fbcf6324d038ad7ab0cf334c11a", "IPY_MODEL_0b20899b053b4718ae2f40e15fa0a795", "IPY_MODEL_792474bcaa8c444d9964da7bde9a4348", "IPY_MODEL_ceec3fe59cab4462afacedca4df5d5d4", "IPY_MODEL_5f47e7aa487647f09c1621a43f88b3df", "IPY_MODEL_a24c24c9808649e187f0667c6533bf81", "IPY_MODEL_76a5eee3daaa4781a5fd6b83ffe3db56"], "layout": "IPY_MODEL_0eb456884fc44ddd9800a917c49cd71c"}}, "d4411c9e62d547f886f3cd7e05642edd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cd548b95e3614721a346360e095bbe92", "style": "IPY_MODEL_dbab64e45afa4eb1bd3845e27255db44", "value": "<h5><b>Additional Outputs</b></h5>"}}, "cd548b95e3614721a346360e095bbe92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dbab64e45afa4eb1bd3845e27255db44": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d3157a4b10db420ab18a48fa1ae9d8a7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce0527aad59f4587a731c346329482f8", "style": "IPY_MODEL_f0af0e2edae44995b48295160ac3f68f", "value": "<div>\n<span>\nThe Drift score is a measure for the difference between two distributions, in this check - the test\nand train distributions.<br> The check shows the drift score and distributions for the features,\nsorted by drift score and showing only the top 5 features,\naccording to drift score.\n</span>\n</div>"}}, "ce0527aad59f4587a731c346329482f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f0af0e2edae44995b48295160ac3f68f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6b84879583e4b0f904d88b7c2ddb340": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_84fd04d932ae4117a6e2c0185e671b7a", "style": "IPY_MODEL_31ac10088f3e41659b27c3aa68b238a4", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "84fd04d932ae4117a6e2c0185e671b7a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "31ac10088f3e41659b27c3aa68b238a4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2fb28fbcf6324d038ad7ab0cf334c11a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b2869a2111d9410fbcb529b3f25b39d1", "style": "IPY_MODEL_2b08aa17fec5429b8945e169dc2b3923", "value": "<div>If available, the plot titles also show the feature importance (FI) rank</div>"}}, "b2869a2111d9410fbcb529b3f25b39d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b08aa17fec5429b8945e169dc2b3923": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b20899b053b4718ae2f40e15fa0a795": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": 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"LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25e93e38df684f2fa6aa5fa52070ae58": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22acadc1d0a04f189a4d4fbdbdef57da": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9ea48317c8e4d8b9f58b96bc4655327", "style": "IPY_MODEL_e67f495c50b640718b24402cefea76f4", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b9ea48317c8e4d8b9f58b96bc4655327": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e67f495c50b640718b24402cefea76f4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3d30f2b912434366bccc7402095b62ae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10f8dd46b29545b0ad14282637c0dcfe", "style": "IPY_MODEL_c04e7166a7ba4068932a296e9522ebcb", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d5518_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d5518_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d5518_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d5518_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d5518_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d5518_\">\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_d5518_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d5518_row0_col1\" class=\"data row0 col1\" >Label drift score < 0.15</td>\n      <td id=\"T_d5518_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 5.47E-3</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "10f8dd46b29545b0ad14282637c0dcfe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c04e7166a7ba4068932a296e9522ebcb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "772a4ecad5684701a4d624c1f73b37b3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_94e91feeeed94876ad35f93aec700d47", "IPY_MODEL_9255e674b6a5445494e863b335e01cda", "IPY_MODEL_a5256bed9b414f989473523a49e91626", "IPY_MODEL_4712c038c548467b8226a333f194aa38", "IPY_MODEL_95922024fd0749c6aa5e8998a0482d77"], "layout": "IPY_MODEL_72b1636ba65c436aaac5b82d0693f3ed"}}, "94e91feeeed94876ad35f93aec700d47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e730c0dad5a0409485af018b27f7f6f4", "style": "IPY_MODEL_ab410970f3ac4c7c898055809a083b6d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e730c0dad5a0409485af018b27f7f6f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab410970f3ac4c7c898055809a083b6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9255e674b6a5445494e863b335e01cda": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_427f6a5fb3e244518ac34840425437d5", "style": "IPY_MODEL_ed45966c7d954888b61ad5d63f39f287", "value": "<div><span>\n                        The Drift score is a measure for the difference between two distributions, in this check -\n                        the test and train distributions.<br> The check shows the drift score\n                        and distributions for the label. </span></div>"}}, "427f6a5fb3e244518ac34840425437d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed45966c7d954888b61ad5d63f39f287": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a5256bed9b414f989473523a49e91626": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0345a58dbba147da882f205ee21b73b7", "style": "IPY_MODEL_82d36d77cb5c407ba63318e7a484b7c9", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "0345a58dbba147da882f205ee21b73b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82d36d77cb5c407ba63318e7a484b7c9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4712c038c548467b8226a333f194aa38": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": 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"model_module_version": "1.2.0", "state": {}}, "a8af95e27a9949f3a8d4403833a8f94a": {"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_a30a84085e354c21ae8bbc8b89953bc5", "IPY_MODEL_257f4cfda0a1457b917d52378c67efa6", "IPY_MODEL_a44ea86691614e7fa04fe0f2b1d08fbb", "IPY_MODEL_73423ffb173542248af8d4e3fe44aed6"], "layout": "IPY_MODEL_1a71c85cea65403fabd28e4106350afb"}}, "a30a84085e354c21ae8bbc8b89953bc5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ba55bad2cf544b75ba569c58644df607", "style": "IPY_MODEL_f2bc4465aadd4e318d28b86c0dbcdc81", "value": "<h4 id=\"MultivariateDrift_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\"><b>Multivariate Drift</b></h4>"}}, "ba55bad2cf544b75ba569c58644df607": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2bc4465aadd4e318d28b86c0dbcdc81": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "257f4cfda0a1457b917d52378c67efa6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fedf8b6c783b4a549a72f3985417a85d", "style": "IPY_MODEL_9fbb823c55e94c2bae9444e7d21d0a77", "value": "<p>    Calculate drift between the entire train and test datasets using a model trained to distinguish between them. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_multivariate_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "fedf8b6c783b4a549a72f3985417a85d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9fbb823c55e94c2bae9444e7d21d0a77": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a44ea86691614e7fa04fe0f2b1d08fbb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_33f206ffb69249759b7cc860b3cb54f9", "style": "IPY_MODEL_179c3f2dbcdb4c29aa17ca6057772de4", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f4172_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4172_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4172_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4172_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4172_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f4172_\">\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_f4172_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f4172_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.25</td>\n      <td id=\"T_f4172_row0_col2\" class=\"data row0 col2\" >Found drift value of: 0.04, corresponding to a domain classifier AUC of: 0.52</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "33f206ffb69249759b7cc860b3cb54f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "179c3f2dbcdb4c29aa17ca6057772de4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "73423ffb173542248af8d4e3fe44aed6": 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Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "b7ce156bdc204698bd4ff28a3ea2b68d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a55ce328cb9c407b99866a4a43d410cc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c67506842d51450ca5f6844d8557f6f9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_15ce7589c11048b4a34239f0d3bfd873", "style": "IPY_MODEL_ea17d1987bfc4d1899b5e0d0f3c5ec3e", "value": "<br><a href=\"#summary_H8AS9IIN9AOCQNQLP1F3XTRNZ-section-JM56U\" style=\"font-size: 14px;\">Go to top</a>"}}, "15ce7589c11048b4a34239f0d3bfd873": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea17d1987bfc4d1899b5e0d0f3c5ec3e": 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.. GENERATED FROM PYTHON SOURCE LINES 122-130

As you can see in the suite's results: the Date Train-Test Leakage check failed,
indicating that we may have a problem in the way we've split our data!
We've mixed up data from two years, causing a leakage of future data
in the training dataset.
Let's fix this.

Fix Data
^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 130-135

.. code-block:: default


    dt_col = data[datetime_name]
    train_df = data[dt_col.dt.year.isin([2017]) & dt_col.dt.month.isin([6,7,8])]
    test_df = data[dt_col.dt.year.isin([2018]) & dt_col.dt.month.isin([6,7,8])]








.. GENERATED FROM PYTHON SOURCE LINES 136-143

.. code-block:: default


    from deepchecks.tabular import Dataset

    # Create the new Datasets
    train_ds = Dataset(train_df, **columns_metadata)
    test_ds = Dataset(test_df, **columns_metadata)








.. GENERATED FROM PYTHON SOURCE LINES 144-147

Re-run Validation Suite
^^^^^^^^^^^^^^^^^^^^^^^^^


.. GENERATED FROM PYTHON SOURCE LINES 148-152

.. code-block:: default


    suite_result = validation_suite.run(train_ds, test_ds)
    suite_result.show()





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

 .. code-block:: none


    Train Test Validation Suite:
    |            | 0/12 [Time: 00:00]
    Train Test Validation Suite:
    |████        | 4/12 [Time: 00:00, Check=String Mismatch Comparison]
    Train Test Validation Suite:
    |███████     | 7/12 [Time: 00:00, Check=Index Train Test Leakage]  
    Train Test Validation Suite:
    |██████████  | 10/12 [Time: 00:06, Check=Feature Drift]          
    Train Test Validation Suite:
    |████████████| 12/12 [Time: 00:07, Check=Multivariate Drift]
                                                                                         

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        ~ You should have received a copy of the GNU Affero General Public License
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red;text-align: center\">\u2716</div></td>\n      <td id=\"T_29212_row0_col1\" class=\"data row0 col1\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_29212_row0_col2\" class=\"data row0 col2\" >Found 1 features with ratio of new categories above threshold: \n{'addr_state': '0.45%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4d353c8d3d354fb6beae2c47e0f9e22e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f42bfa4ee1a4ff88389c29d4cc6fbd8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a7be4bf79254a15bd73e81e19969755": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4915539929df43f6aff54908eed93aaf", "IPY_MODEL_6606a758e53642beb9ee8f9c58dd5382", 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"IPY_MODEL_203cbcfaacc14a35a12cb68168fee2a2", "value": "<style type=\"text/css\">\n#T_c5356_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5356_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5356_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5356_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c5356_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c5356_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" ># New Categories</th>\n      <th class=\"col_heading level0 col1\" >Ratio of New Categories</th>\n      <th class=\"col_heading level0 col2\" >New Categories Names</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >Feature Name</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_c5356_level0_row0\" class=\"row_heading level0 row0\" >addr_state</th>\n      <td id=\"T_c5356_row0_col0\" class=\"data row0 col0\" >1</td>\n      <td id=\"T_c5356_row0_col1\" class=\"data row0 col1\" >0.45%</td>\n      <td id=\"T_c5356_row0_col2\" class=\"data row0 col2\" >['WV']</td>\n    </tr>\n    <tr>\n      <th id=\"T_c5356_level0_row1\" class=\"row_heading level0 row1\" >purpose</th>\n      <td id=\"T_c5356_row1_col0\" class=\"data row1 col0\" >0</td>\n      <td id=\"T_c5356_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_c5356_row1_col2\" class=\"data row1 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_c5356_level0_row2\" class=\"row_heading level0 row2\" >home_ownership</th>\n      <td id=\"T_c5356_row2_col0\" class=\"data row2 col0\" >0</td>\n      <td id=\"T_c5356_row2_col1\" class=\"data row2 col1\" >0%</td>\n      <td id=\"T_c5356_row2_col2\" class=\"data row2 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_c5356_level0_row3\" class=\"row_heading level0 row3\" >application_type</th>\n      <td id=\"T_c5356_row3_col0\" class=\"data row3 col0\" >0</td>\n      <td id=\"T_c5356_row3_col1\" class=\"data row3 col1\" >0%</td>\n      <td id=\"T_c5356_row3_col2\" class=\"data row3 col2\" >[]</td>\n    </tr>\n    <tr>\n      <th id=\"T_c5356_level0_row4\" class=\"row_heading level0 row4\" >verification_status</th>\n      <td id=\"T_c5356_row4_col0\" class=\"data row4 col0\" >0</td>\n      <td id=\"T_c5356_row4_col1\" class=\"data row4 col1\" >0%</td>\n      <td id=\"T_c5356_row4_col2\" class=\"data row4 col2\" >[]</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "86b023a8a5b04084bd5389c0ebae8199": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "203cbcfaacc14a35a12cb68168fee2a2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab43a9f7e8a8470c80754c85b76cb993": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0711e17d445c4b258d5ffd333b176ae1", "style": "IPY_MODEL_5cea42f9d595451192ec3146d8e9c471", "value": "<br><a href=\"#summary_48I2CQ6STA0HORTR9USBSFVG9-section-CTCHX\" style=\"font-size: 14px;\">Go to top</a>"}}, "0711e17d445c4b258d5ffd333b176ae1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5cea42f9d595451192ec3146d8e9c471": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ee757ff9c854943a3016d7873475e8a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "565ce07d5c8548e790eb154ea7ef2290": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e7c956865844507bc062f1d3d2f2cbe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ebc0900c58cf4d05b7a5f34f9a1878da", "style": "IPY_MODEL_dd3b11d21dcf4d28a61f13ae08a18d51", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "ebc0900c58cf4d05b7a5f34f9a1878da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd3b11d21dcf4d28a61f13ae08a18d51": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4385ca28ba7a49929b7889563c6a5bf8": {"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_620646e6520546e1b86e28ea15a18a7a", "IPY_MODEL_1c9e0f22ee2e4144a888cc6256edf338", "IPY_MODEL_3c52aa62b469434191caf8f1581ddc31", "IPY_MODEL_4cd556a087bf4671bcef3de2df662b90"], "layout": "IPY_MODEL_7aa1091e700e4ce39f39e2002f2391d8"}}, "620646e6520546e1b86e28ea15a18a7a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_553de7ffbd894daebc9c3d387f5a8dc6", "style": "IPY_MODEL_52af0dc334fc4ef68e48cd3cdf01b1db", "value": "<h4 id=\"MultivariateDrift_48I2CQ6STA0HORTR9USBSFVG9-section-CTCHX\"><b>Multivariate Drift</b></h4>"}}, "553de7ffbd894daebc9c3d387f5a8dc6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52af0dc334fc4ef68e48cd3cdf01b1db": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1c9e0f22ee2e4144a888cc6256edf338": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3d2b8b79e3ba413caebb145becfb61f6", "style": "IPY_MODEL_a049f67cbe584df6a8ce1ef43bfb2eb7", "value": "<p>    Calculate drift between the entire train and test datasets using a model trained to distinguish between them. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_multivariate_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3d2b8b79e3ba413caebb145becfb61f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a049f67cbe584df6a8ce1ef43bfb2eb7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c52aa62b469434191caf8f1581ddc31": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_422a2217b8db47569a768617987cd262", "style": "IPY_MODEL_4d3e97d4c0e94978af4be9e1ee2366db", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_ee906_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee906_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee906_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee906_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ee906_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ee906_\">\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_ee906_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_ee906_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.25</td>\n      <td id=\"T_ee906_row0_col2\" class=\"data row0 col2\" >Found drift value of: 0.47, corresponding to a domain classifier AUC of: 0.73</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "422a2217b8db47569a768617987cd262": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d3e97d4c0e94978af4be9e1ee2366db": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4cd556a087bf4671bcef3de2df662b90": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b7da2f43c88c4d8fbbc0e3d4ae3d7f92", "IPY_MODEL_0f030dced0fe4cdca57becb50330c775", "IPY_MODEL_4f602ced054b46b2ac1cecfc562a66cc", "IPY_MODEL_385251e476ed42c99f6db16429e31326", "IPY_MODEL_3c813b9b0932412dbfbd701d28f736f8", "IPY_MODEL_6e2a6e4ce19741eb85f522f6e7c4fded", "IPY_MODEL_6043bd0bce114ca1a695cb8b80b69599", "IPY_MODEL_c300ab119d9947099522088d03856b22", "IPY_MODEL_48d6d93474224b979baf2296bc24fe9f", "IPY_MODEL_f6d81b5331334c29b86f8371d0e9460c", "IPY_MODEL_b4632a2fdcb441e0a84acb42acfa34a1"], "layout": "IPY_MODEL_296baf41c78d4412b23f1327e795cf63"}}, "b7da2f43c88c4d8fbbc0e3d4ae3d7f92": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3aa5ed97c14749cea711699e2be42692", "style": "IPY_MODEL_11252ebdfbcc486993107de2a64fee89", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3aa5ed97c14749cea711699e2be42692": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "11252ebdfbcc486993107de2a64fee89": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f030dced0fe4cdca57becb50330c775": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e5adda607a34c9ab73c0959071fc40b", "style": "IPY_MODEL_832ed99e58854ce4a530c99a72d6e407", "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>"}}, "4e5adda607a34c9ab73c0959071fc40b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "832ed99e58854ce4a530c99a72d6e407": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f602ced054b46b2ac1cecfc562a66cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4af3e00480ac45cfb67f6fefe53d4400", "style": "IPY_MODEL_0c336d70d99f4bf899d663863a765832", "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>"}}, "4af3e00480ac45cfb67f6fefe53d4400": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c336d70d99f4bf899d663863a765832": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "385251e476ed42c99f6db16429e31326": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, 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{}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "PV3C0idulj8UlPzXHSSYP5o3Chu1d6s/56P1zXY0sD97ifmXxiexPw1y4Mf2hbc/MBjp5Y5jkz/syPRDfj2WPxSU/NcdJJg/xfuqFkm6cD8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "nDOG6Ik+pD+2u/YoJremPz1cIQbdn6Q/E5Y+cbPAqz9X5tPfDrWqPwVTpx2wUqw/qXe+CNh6pT+cMuUbP3CiP0yeF42VP6I/tL7ZjgYijD8="}]}, "f6d81b5331334c29b86f8371d0e9460c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb2351d3919a475c891fc7ca0a66acf8", "style": "IPY_MODEL_64976831acb54293ba96beac0b600005", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Running on 10000 <b>train</b> data samples out of 16281. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "fb2351d3919a475c891fc7ca0a66acf8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64976831acb54293ba96beac0b600005": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4632a2fdcb441e0a84acb42acfa34a1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e51c4523b4804bc8aece34eea59987e4", "style": "IPY_MODEL_cd09a437ccab46fc821e61eccc118eae", "value": "<br><a href=\"#summary_48I2CQ6STA0HORTR9USBSFVG9-section-CTCHX\" style=\"font-size: 14px;\">Go to top</a>"}}, "e51c4523b4804bc8aece34eea59987e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd09a437ccab46fc821e61eccc118eae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "296baf41c78d4412b23f1327e795cf63": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7aa1091e700e4ce39f39e2002f2391d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8608d7ccc4e4a79819fa4c1f2ab941c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64602986aaf84c71955f3f267d87627e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "926b7209df4d4bb18d633eb0ef015bcd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3fe73be8d26846d0870b48d70b7ec54d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a8d42eaf3382472e8d9b6bbb28d5fdfa", "IPY_MODEL_fcd5991970284c5f9c9d3948f35d016a"], "layout": "IPY_MODEL_26f139396a1f478e8d044472f3dfa1ad"}}, "a8d42eaf3382472e8d9b6bbb28d5fdfa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ab2787a79e66449497444912e9b43daf", "style": "IPY_MODEL_26ba2bfe83634afea7b82c0dfbdc79c4", "value": "<span id=\"summary_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\"></span>"}}, "ab2787a79e66449497444912e9b43daf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26ba2bfe83634afea7b82c0dfbdc79c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fcd5991970284c5f9c9d3948f35d016a": {"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_017d266e6e4e413eb9e0b37e0fc6dee0"], "layout": "IPY_MODEL_a33b358609b54a9c8693a9bd0a47d114", "selected_index": null}}, "017d266e6e4e413eb9e0b37e0fc6dee0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_182e2bba34bf4dce89f946b81aa4733d", "IPY_MODEL_f96b27d7bb4c44fb859fae147ed4af17", "IPY_MODEL_0d6808ec5b8b40569b9c92a7e774b2a9", "IPY_MODEL_2fa98174549d427fa50062f540d038ef", "IPY_MODEL_1b09692fccf645dab21bd424f2c36a8a", "IPY_MODEL_2fa98174549d427fa50062f540d038ef", "IPY_MODEL_1121e97319cf4ce5b90365a815751e0a", "IPY_MODEL_2fa98174549d427fa50062f540d038ef", "IPY_MODEL_8bb5a7497dad433c97984df6a9eb5c0f"], "layout": "IPY_MODEL_9ebac44331dd45fa976bb7a1dc556293"}}, "182e2bba34bf4dce89f946b81aa4733d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_165c318cb5544f74908ea8dedd7acd12", "style": "IPY_MODEL_4d34abc270a64904907b85c4d8062971", "value": "<style type=\"text/css\">\n#T_19621_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19621_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19621_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19621_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19621_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_19621_\">\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_19621_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\">Datasets Size Comparison</a></td>\n      <td id=\"T_19621_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_19621_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.33</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row1_col1\" class=\"data row1 col1\" ><a href=\"#FeatureLabelCorrelationChange_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\">Feature Label Correlation Change</a></td>\n      <td id=\"T_19621_row1_col2\" class=\"data row1 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_19621_row1_col3\" class=\"data row1 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row2_col1\" class=\"data row2 col1\" ><a href=\"#FeatureDrift_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\">Feature Drift</a></td>\n      <td id=\"T_19621_row2_col2\" class=\"data row2 col2\" >categorical drift score < 0.2 and numerical drift score < 0.2</td>\n      <td id=\"T_19621_row2_col3\" class=\"data row2 col3\" >Passed for 24 columns out of 24 columns.\nFound column \"sub_grade\" has the highest categorical drift score: 0.14\nFound column \"int_rate\" has the highest numerical drift score: 0.12</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row3_col1\" class=\"data row3 col1\" ><a href=\"#FeatureLabelCorrelationChange_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\">Feature Label Correlation Change</a></td>\n      <td id=\"T_19621_row3_col2\" class=\"data row3 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_19621_row3_col3\" class=\"data row3 col3\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row4_col1\" class=\"data row4 col1\" ><a href=\"#LabelDrift_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\">Label Drift</a></td>\n      <td id=\"T_19621_row4_col2\" class=\"data row4 col2\" >Label drift score < 0.15</td>\n      <td id=\"T_19621_row4_col3\" class=\"data row4 col3\" >Label's drift score Cramer's V is 0.02</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row5_col1\" class=\"data row5 col1\" >Date Train-Test Leakage (duplicates)</td>\n      <td id=\"T_19621_row5_col2\" class=\"data row5 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_19621_row5_col3\" class=\"data row5 col3\" >No leaked dates found</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row6_col1\" class=\"data row6 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_19621_row6_col2\" class=\"data row6 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_19621_row6_col3\" class=\"data row6 col3\" >Passed for 8 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row7_col1\" class=\"data row7 col1\" >New Label Train Test</td>\n      <td id=\"T_19621_row7_col2\" class=\"data row7 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_19621_row7_col3\" class=\"data row7 col3\" >Found 0 new labels in test data: []</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row8_col1\" class=\"data row8 col1\" >Train Test Samples Mix</td>\n      <td id=\"T_19621_row8_col2\" class=\"data row8 col2\" >Percentage of test data samples that appear in train data is less or equal to 5%</td>\n      <td id=\"T_19621_row8_col3\" class=\"data row8 col3\" >No samples mix found</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row9_col1\" class=\"data row9 col1\" >Index Train-Test Leakage</td>\n      <td id=\"T_19621_row9_col2\" class=\"data row9 col2\" >Ratio of leaking indices is less or equal to 0%</td>\n      <td id=\"T_19621_row9_col3\" class=\"data row9 col3\" >No index leakage found</td>\n    </tr>\n    <tr>\n      <td id=\"T_19621_row10_col0\" class=\"data row10 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19621_row10_col1\" class=\"data row10 col1\" >Date Train-Test Leakage (overlap)</td>\n      <td id=\"T_19621_row10_col2\" class=\"data row10 col2\" >Date leakage ratio is less or equal to 0%</td>\n      <td id=\"T_19621_row10_col3\" class=\"data row10 col3\" >No leaked dates found</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "165c318cb5544f74908ea8dedd7acd12": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d34abc270a64904907b85c4d8062971": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f96b27d7bb4c44fb859fae147ed4af17": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_503f7b228e2c4b509c675578265d9805", "style": "IPY_MODEL_736171ce08e043a895622b14efc22376", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "503f7b228e2c4b509c675578265d9805": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "736171ce08e043a895622b14efc22376": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d6808ec5b8b40569b9c92a7e774b2a9": {"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_39048ffc83f74aa495da869a04c4cb7c", "IPY_MODEL_8996a2e40c7c4ecab55fbfea8ba5b370", "IPY_MODEL_b192e052673e4e7bbca4a64cc16d420f", "IPY_MODEL_18a0325a04264cf08ae5764ab1f2ff14"], "layout": "IPY_MODEL_2eeb7599b16e4a31ac8809b99427cbbd"}}, "39048ffc83f74aa495da869a04c4cb7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3044b26d8ecf4fc69ffdddc2a94be568", "style": "IPY_MODEL_95ed6184285b422d94e30c8ebb9f4089", "value": "<h4 id=\"DatasetsSizeComparison_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\"><b>Datasets Size Comparison</b></h4>"}}, "3044b26d8ecf4fc69ffdddc2a94be568": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95ed6184285b422d94e30c8ebb9f4089": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8996a2e40c7c4ecab55fbfea8ba5b370": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07676e5578684e78b4a14973978910ad", "style": "IPY_MODEL_a861f68ce5fe44e997d75a5347145dae", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_datasets_size_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "07676e5578684e78b4a14973978910ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a861f68ce5fe44e997d75a5347145dae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b192e052673e4e7bbca4a64cc16d420f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7cb0cb973b0e4806b3417deb51949ffd", "style": "IPY_MODEL_7e9310047c664be3a7764895ef5da502", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_64874_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64874_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64874_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64874_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64874_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_64874_\">\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_64874_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_64874_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_64874_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.33</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7cb0cb973b0e4806b3417deb51949ffd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e9310047c664be3a7764895ef5da502": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18a0325a04264cf08ae5764ab1f2ff14": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_371e49d39e0a42e3850f340617e99226", "IPY_MODEL_d7cef85ca63e44d882974fbf13e38458", "IPY_MODEL_49fc67cb1c9b432381b56287d6525328"], "layout": "IPY_MODEL_b62b579306c24e80909eb09afcdafbd6"}}, "371e49d39e0a42e3850f340617e99226": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_85851828218848d7b84731685fb6212b", "style": "IPY_MODEL_501121cfcc3d432ca28dedc95828406d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "85851828218848d7b84731685fb6212b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "501121cfcc3d432ca28dedc95828406d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d7cef85ca63e44d882974fbf13e38458": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_394c14b1187f4d40b6b40722212a5519", "style": "IPY_MODEL_470ffbdc4cde4fdd9eb71dd3495ca586", "value": "<style type=\"text/css\">\n#T_ae0b9_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ae0b9_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ae0b9_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ae0b9_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_ae0b9_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_ae0b9_\">\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_ae0b9_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_ae0b9_row0_col0\" class=\"data row0 col0\" >16281</td>\n      <td id=\"T_ae0b9_row0_col1\" class=\"data row0 col1\" >5387</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "394c14b1187f4d40b6b40722212a5519": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "470ffbdc4cde4fdd9eb71dd3495ca586": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49fc67cb1c9b432381b56287d6525328": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82afb9601b9c498787230b53f076116d", "style": "IPY_MODEL_c3833978ca8d4000b2bf4f41c46178f5", "value": "<br><a href=\"#summary_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\" style=\"font-size: 14px;\">Go to top</a>"}}, "82afb9601b9c498787230b53f076116d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3833978ca8d4000b2bf4f41c46178f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b62b579306c24e80909eb09afcdafbd6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2eeb7599b16e4a31ac8809b99427cbbd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2fa98174549d427fa50062f540d038ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8da1a816c5aa46018f320d70dbfdbe06", "style": "IPY_MODEL_3dbaa863df204e1d9c7728404e67817e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8da1a816c5aa46018f320d70dbfdbe06": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3dbaa863df204e1d9c7728404e67817e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1b09692fccf645dab21bd424f2c36a8a": {"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_83a5637ad2024d1f9f3d12f25412d708", "IPY_MODEL_96388207c3af4d3daef06168e6ab57e5", "IPY_MODEL_ab880b11f4ee45288509b81a4e54aa44", "IPY_MODEL_aac5c06ec10c484492be6dfad0385a0f"], "layout": "IPY_MODEL_535690afa36d443196211bfee44d83e1"}}, "83a5637ad2024d1f9f3d12f25412d708": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5793e73e8fc5476dbc8481c633ad884e", "style": "IPY_MODEL_3b76891ba02c4dffbd8a81ec2ef66875", "value": "<h4 id=\"FeatureLabelCorrelationChange_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\"><b>Feature Label Correlation Change</b></h4>"}}, "5793e73e8fc5476dbc8481c633ad884e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3b76891ba02c4dffbd8a81ec2ef66875": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96388207c3af4d3daef06168e6ab57e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5cb8c8518c3844ab8122998d2ec14707", "style": "IPY_MODEL_f4984085726d422e8113861fa8f75e2b", "value": "<p>    Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5cb8c8518c3844ab8122998d2ec14707": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f4984085726d422e8113861fa8f75e2b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab880b11f4ee45288509b81a4e54aa44": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_423c7ab878f04cadb3e225843fb9ef92", "style": "IPY_MODEL_fa23c37e97b74264aa39c50ae687d57a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c26d6_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c26d6_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c26d6_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c26d6_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c26d6_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c26d6_\">\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_c26d6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c26d6_row0_col1\" class=\"data row0 col1\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_c26d6_row0_col2\" class=\"data row0 col2\" >Passed for 24 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_c26d6_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c26d6_row1_col1\" class=\"data row1 col1\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_c26d6_row1_col2\" class=\"data row1 col2\" >Passed for 24 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "423c7ab878f04cadb3e225843fb9ef92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa23c37e97b74264aa39c50ae687d57a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aac5c06ec10c484492be6dfad0385a0f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_bcf8dcafbe1844f08afaafa768e58444", "IPY_MODEL_00f8677f57ca4a4d986ac89b34277637", "IPY_MODEL_7bde964923bb47d9a9abbae9f43ef717", "IPY_MODEL_a9733f05b00f471a82475fb44983aafc", "IPY_MODEL_093c0824e16b47a9b906ee7d67dc8ab6", "IPY_MODEL_f4ab62dfc3524b9893ac1818d367ff5f", "IPY_MODEL_e4133124baa7420d984223758fcb3216", "IPY_MODEL_7d3797febad64b08a4ae20379c761fe7", "IPY_MODEL_cd9aa0c04e90494d9341ff51fb890398", "IPY_MODEL_3b48a43ad35f40b492621e5cfd17df6c", "IPY_MODEL_b666277ce9864c1988f354cdaf5605a7", "IPY_MODEL_1f7d7a099c7748b5aabf73f245340dac", "IPY_MODEL_9af2b0b52f1240adb42154e61f81ad29"], "layout": "IPY_MODEL_0970be406109410dbc4ad0f736cb0735"}}, "bcf8dcafbe1844f08afaafa768e58444": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1eff34b937da4b58aa8e9d1d8af76d9a", "style": "IPY_MODEL_86e17ff923bb4a6ca11d3d7a41e0dbff", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1eff34b937da4b58aa8e9d1d8af76d9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "86e17ff923bb4a6ca11d3d7a41e0dbff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "00f8677f57ca4a4d986ac89b34277637": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, 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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": "DwzG9h/Hwj8PDMb2H8fCPwzLK3Y7aLU/9c+K1aUXoD/62EykvuKoPw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "yNViTFTrlz9UyyLclNysP4ambE8tlYo/AAAAAAAAAAA3WaSApaKZPw=="}]}, "7bde964923bb47d9a9abbae9f43ef717": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f3bcbb6f6f6443fdb63fe56b8f635ac5", "style": "IPY_MODEL_71f082e6dc3646f48128c8860fdedbd8", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/tabular/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "f3bcbb6f6f6443fdb63fe56b8f635ac5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71f082e6dc3646f48128c8860fdedbd8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9733f05b00f471a82475fb44983aafc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e9cc46a2b64248df8f19a5f50d29395b", "style": "IPY_MODEL_2345f2bcbf48488d860bb210b8f9672f", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "e9cc46a2b64248df8f19a5f50d29395b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2345f2bcbf48488d860bb210b8f9672f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "093c0824e16b47a9b906ee7d67dc8ab6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2cbfaa2e02684dfa919a9f6e3ef7a7bf", "style": "IPY_MODEL_18bfd3d319914d52b188883fff24cc8b", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "2cbfaa2e02684dfa919a9f6e3ef7a7bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "18bfd3d319914d52b188883fff24cc8b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f4ab62dfc3524b9893ac1818d367ff5f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a300833271f4d6186a5ad6d418b3d77", "style": "IPY_MODEL_78b3e4efcd274dc597de6c7f42e3b95d", "value": "<div>Can indicate that this feature's success in predicting the label is actually due to data leakage, </div>"}}, "0a300833271f4d6186a5ad6d418b3d77": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "78b3e4efcd274dc597de6c7f42e3b95d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4133124baa7420d984223758fcb3216": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e9c1e528dab478c97e1aa6ec82150ec", "style": "IPY_MODEL_69bdd216e1c045a2817454df7bd85334", "value": "<div>   meaning that the feature holds information that is based on the label to begin with.</div>"}}, "2e9c1e528dab478c97e1aa6ec82150ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "69bdd216e1c045a2817454df7bd85334": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d3797febad64b08a4ae20379c761fe7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0ba366ce20d748cd94f5e71e7aecce3d", "style": "IPY_MODEL_3bcce2a284c54d659e2a4f9c0dec42a3", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "0ba366ce20d748cd94f5e71e7aecce3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bcce2a284c54d659e2a4f9c0dec42a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cd9aa0c04e90494d9341ff51fb890398": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c9bdecccfdf4e22814a10c45ccd2a97", "style": "IPY_MODEL_75c1f8a409b849729bfe58833aa4b2f5", "value": "<div>   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test </div>"}}, "0c9bdecccfdf4e22814a10c45ccd2a97": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "75c1f8a409b849729bfe58833aa4b2f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b48a43ad35f40b492621e5cfd17df6c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53bd36adbf9e4320ae7f492d4bb3abfd", "style": "IPY_MODEL_672cb93296d940fab4555cbf621d0e99", "value": "<div>   can be explained by leakage in train that is not relevant to a new dataset.</div>"}}, "53bd36adbf9e4320ae7f492d4bb3abfd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "672cb93296d940fab4555cbf621d0e99": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b666277ce9864c1988f354cdaf5605a7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a6badea998244c9dad94e2d0fde001f6", "style": "IPY_MODEL_51af48b496f14a8eae06013ec285b505", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "a6badea998244c9dad94e2d0fde001f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51af48b496f14a8eae06013ec285b505": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1f7d7a099c7748b5aabf73f245340dac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2acf004e87e3421db742d98f73b95af6", "style": "IPY_MODEL_8fd6f57266a24cc6b6f8d5fa262072e1", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "2acf004e87e3421db742d98f73b95af6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8fd6f57266a24cc6b6f8d5fa262072e1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9af2b0b52f1240adb42154e61f81ad29": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e872613b974a4524a80067889bca5096", "style": "IPY_MODEL_03a0a5400afd457abe06e5aed2507a0e", "value": "<br><a href=\"#summary_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\" style=\"font-size: 14px;\">Go to top</a>"}}, "e872613b974a4524a80067889bca5096": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03a0a5400afd457abe06e5aed2507a0e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0970be406109410dbc4ad0f736cb0735": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "535690afa36d443196211bfee44d83e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1121e97319cf4ce5b90365a815751e0a": {"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_d47b686525904975907b31b9ef92472e", "IPY_MODEL_e0d298dcab3e45e399cba83a18301161", "IPY_MODEL_69197b0bba5841269e364e3809c22615", "IPY_MODEL_69bde7aa9c794ea1bb22144247e97f67"], "layout": "IPY_MODEL_a14320d4c57a4c8cb653b60af59289a5"}}, "d47b686525904975907b31b9ef92472e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d5bf78f2abaf4e969a00eae26dcb9afb", "style": "IPY_MODEL_aa48205e262849299a15b26b4e89a6f4", "value": "<h4 id=\"FeatureDrift_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\"><b>Feature Drift</b></h4>"}}, "d5bf78f2abaf4e969a00eae26dcb9afb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa48205e262849299a15b26b4e89a6f4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0d298dcab3e45e399cba83a18301161": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_99c76b6139fa4bd7a41f923276cdd496", "style": "IPY_MODEL_f46feb575a81452ca7d6570337c4fca2", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "99c76b6139fa4bd7a41f923276cdd496": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f46feb575a81452ca7d6570337c4fca2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "69197b0bba5841269e364e3809c22615": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_06a14327f5284a6a97a128c8a43585bc", "style": "IPY_MODEL_dc217fb003604ff0ae528958887477f3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f3860_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3860_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3860_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3860_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3860_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f3860_\">\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_f3860_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f3860_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.2</td>\n      <td id=\"T_f3860_row0_col2\" class=\"data row0 col2\" >Passed for 24 columns out of 24 columns.\nFound column \"sub_grade\" has the highest categorical drift score: 0.14\nFound column \"int_rate\" has the highest numerical drift score: 0.12</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "06a14327f5284a6a97a128c8a43585bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc217fb003604ff0ae528958887477f3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "69bde7aa9c794ea1bb22144247e97f67": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2a8c15a10a18448c83090378f3ae2981", "IPY_MODEL_ba0fc4d02346494d9e37f8a5af00441e", "IPY_MODEL_4f1d773240ac4e719e3f3cdb89088a91", "IPY_MODEL_d40e9e9ce5e34e07acfebdb936656954", "IPY_MODEL_84f190c5a60843a984cf0a55ea2efeca", "IPY_MODEL_4347e42cf21a4b5295e603a7f67dbc65", "IPY_MODEL_820777d3a48c44b683d6d8a988e79863", "IPY_MODEL_9823c0ab268d4e16bbbd820c322d3aa7", "IPY_MODEL_f5b8723769f844ae91a35dddee6c51cf", "IPY_MODEL_224de81c90f54afc8983032edec4726f"], "layout": "IPY_MODEL_f17af9a4d8444cda8b6ac0a316f778fb"}}, "2a8c15a10a18448c83090378f3ae2981": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f8141192e1a44ac8ae0a1faef6437652", "style": "IPY_MODEL_ebea2b9873fc4ffcbd11df0527c73f7d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f8141192e1a44ac8ae0a1faef6437652": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ebea2b9873fc4ffcbd11df0527c73f7d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ba0fc4d02346494d9e37f8a5af00441e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82111b964160410e8ead81c9d1468bbc", "style": "IPY_MODEL_30cf9faa07c84c68bcdec5ef5e6ab276", "value": "<div>\n<span>\nThe Drift score is a measure for the difference between two distributions, in this check - the test\nand train distributions.<br> The check shows the drift score and distributions for the features,\nsorted by drift score and showing only the top 5 features,\naccording to drift score.\n</span>\n</div>"}}, "82111b964160410e8ead81c9d1468bbc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30cf9faa07c84c68bcdec5ef5e6ab276": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f1d773240ac4e719e3f3cdb89088a91": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_31b4a566352a46258ff2da679a21cc9c", "style": "IPY_MODEL_a1fb030069d84e5cbd34e58b38bef42c", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "31b4a566352a46258ff2da679a21cc9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1fb030069d84e5cbd34e58b38bef42c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d40e9e9ce5e34e07acfebdb936656954": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db11df2a50f049baa12a2c3835e8d450", "style": "IPY_MODEL_b35035db12ae41799ae98eb6773dc01e", "value": "<div>If available, the plot titles also show the feature importance (FI) rank</div>"}}, "db11df2a50f049baa12a2c3835e8d450": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b35035db12ae41799ae98eb6773dc01e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "84f190c5a60843a984cf0a55ea2efeca": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": 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"model_module_version": "1.2.0", "state": {}}, "8bb5a7497dad433c97984df6a9eb5c0f": {"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_6a37d0f7f16a43c087e2163a46894c7d", "IPY_MODEL_b194279d397b4a2798aff5479a88b569", "IPY_MODEL_4eb5b68e121046699bf706d4c5958f5e", "IPY_MODEL_96b8c53ce15a4ebd86a2cfd3d619f9e4"], "layout": "IPY_MODEL_a1c1f96428ca4a23a948284a7b68cd60"}}, "6a37d0f7f16a43c087e2163a46894c7d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9b234f373e3b443db6c86b7299df2d4c", "style": "IPY_MODEL_4b3cd290aeb94240b9993da53469fe34", "value": "<h4 id=\"LabelDrift_48I2CQ6STA0HORTR9USBSFVG9-section-BI0AN\"><b>Label Drift</b></h4>"}}, "9b234f373e3b443db6c86b7299df2d4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b3cd290aeb94240b9993da53469fe34": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b194279d397b4a2798aff5479a88b569": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f8ad013890c94dafbef47aabf716d9b5", "style": "IPY_MODEL_0519c3c71809484c9f0bf8b14fdbd0e3", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f8ad013890c94dafbef47aabf716d9b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0519c3c71809484c9f0bf8b14fdbd0e3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4eb5b68e121046699bf706d4c5958f5e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_666448c187e54784ac4e2b942fac43e1", "style": "IPY_MODEL_2496d71f0d604e37b000d1524315d74e", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_dc188_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc188_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc188_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc188_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc188_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_dc188_\">\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_dc188_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_dc188_row0_col1\" class=\"data row0 col1\" >Label drift score < 0.15</td>\n      <td id=\"T_dc188_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 0.02</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "666448c187e54784ac4e2b942fac43e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2496d71f0d604e37b000d1524315d74e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96b8c53ce15a4ebd86a2cfd3d619f9e4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a84d5d4be21944e1a3ca33881657ee58", "IPY_MODEL_633fb5da3c964c9fba941329bd785b42", "IPY_MODEL_bc038e4f194f42eaa611d497fdb06c50", "IPY_MODEL_98c792bb834944e7805c99e67559d968", "IPY_MODEL_06e25f4c258b4a06b999c507526a5387"], "layout": "IPY_MODEL_6965927634d74fbfafd3aff847a115f4"}}, "a84d5d4be21944e1a3ca33881657ee58": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d5bd032f296149d383e92beb2077c356", "style": "IPY_MODEL_1fb7204369d6475a8233074480af8dc0", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d5bd032f296149d383e92beb2077c356": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1fb7204369d6475a8233074480af8dc0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "633fb5da3c964c9fba941329bd785b42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5c83eb23005464d9e1d49a91226a351", "style": "IPY_MODEL_1c58e687e0f54c08a53c8eb1acd2817a", "value": "<div><span>\n                        The Drift score is a measure for the difference between two distributions, in this check -\n                        the test and train distributions.<br> The check shows the drift score\n                        and distributions for the label. </span></div>"}}, "a5c83eb23005464d9e1d49a91226a351": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1c58e687e0f54c08a53c8eb1acd2817a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bc038e4f194f42eaa611d497fdb06c50": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9e55b1e6a7d94532b866b2b89d1f1dff", "style": "IPY_MODEL_413ff6678fa0483e80098371cac63ff0", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "9e55b1e6a7d94532b866b2b89d1f1dff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "413ff6678fa0483e80098371cac63ff0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "98c792bb834944e7805c99e67559d968": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": 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.. GENERATED FROM PYTHON SOURCE LINES 153-172

Ok, the date leakage doesn't happen anymore!

However, in the current split after the fix, we can see that we have a multivariate drift, 
detected by the :ref:`tabular__multivariate_drift` check.
The drift is caused mainly by a combination of features representing the loan's interest rate (``int_rate``) 
and its grade (``sub_grade``). In order to proceed, we should think about the two options we have: 
To split the data in a different manner, or to stay with the current split.

For working with different data splits: We can consider examining other sampling techniques 
(e.g. using only data from the same year), ideally achieving one in which the training data's
univariate and multivariate distribution is similar to the data 
on which the model will run (test / production data). 
Of course, we can use deepchecks to validate the new splits.

If the current split is representative and we are planning on training a model with it, 
it is worth understanding this drift (do we expect this kind of drift in the model's 
production environment? can we do something about it?).

For more details about drift, see the :ref:`drift_user_guide`.

.. GENERATED FROM PYTHON SOURCE LINES 177-181

Run a Single Check
-------------------

We can run a single check on a dataset, and see the results.

.. GENERATED FROM PYTHON SOURCE LINES 181-190

.. code-block:: default


    # If we want to run only that check (possible with or without condition)
    from deepchecks.tabular.checks import MultivariateDrift

    check_with_condition = MultivariateDrift().add_condition_overall_drift_value_less_than(0.4)
    # or just the check without the condition:
    # check = MultivariateDrift()
    dataset_drift_result = check_with_condition.run(train_ds, test_ds)








.. GENERATED FROM PYTHON SOURCE LINES 191-192

We can also inspect and use the result's value:

.. GENERATED FROM PYTHON SOURCE LINES 192-195

.. code-block:: default


    dataset_drift_result.value





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

 .. code-block:: none


    {'domain_classifier_auc': 0.7328426082404159, 'domain_classifier_drift_score': 0.46568521648083183, 'domain_classifier_feature_importance': {'int_rate': 0.5092728878422134, 'sub_grade': 0.38754783632616996, 'dti': 0.031056814836620684, 'revol_bal': 0.030762437444804377, 'initial_list_status': 0.02458051221666191, 'emp_length': 0.012952605239917654, 'pub_rec': 0.0038269060936120774, 'fico_range_low': -0.0, 'mort_acc': 0.0, 'annual_inc': 0.0, 'total_acc': 0.0, 'revol_util': 0.0, 'fico_range_high': 0.0, 'pub_rec_bankruptcies': 0.0, 'loan_amnt': 0.0, 'installment': 0.0, 'time_to_earliest_cr_line': 0.0, 'open_acc': 0.0, 'application_type': 0.0, 'addr_state': 0.0, 'home_ownership': 0.0, 'purpose': 0.0, 'term': 0.0, 'verification_status': 0.0}}



.. GENERATED FROM PYTHON SOURCE LINES 196-197

and see if the conditions have passed

.. GENERATED FROM PYTHON SOURCE LINES 197-199

.. code-block:: default

    dataset_drift_result.passed_conditions()





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

 .. code-block:: none


    False



.. GENERATED FROM PYTHON SOURCE LINES 200-204

Create a Custom Suite
----------------------

To create our own suite, we can simply write all of the checks, and add optional conditions.

.. GENERATED FROM PYTHON SOURCE LINES 204-217

.. code-block:: default


    from deepchecks.tabular import Suite
    from deepchecks.tabular.checks import FeatureDrift, MultivariateDrift, \
     PredictionDrift, LabelDrift

    drift_suite = Suite('drift suite',
    FeatureDrift().add_condition_drift_score_less_than(
      max_allowed_categorical_score=0.2, max_allowed_numeric_score=0.1),
    MultivariateDrift().add_condition_overall_drift_value_less_than(0.4),
    LabelDrift(),
    PredictionDrift()
    )








.. GENERATED FROM PYTHON SOURCE LINES 218-219

we can run our new suite using:

.. GENERATED FROM PYTHON SOURCE LINES 220-224

.. code-block:: default


    result = drift_suite.run(train_ds, test_ds)
    result.show()





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

 .. code-block:: none


    drift suite:
    |     | 0/4 [Time: 00:00]
    drift suite:
    |█▎   | 1/4 [Time: 00:02, Check=Feature Drift]
    drift suite:
    |██▌  | 2/4 [Time: 00:03, Check=Multivariate Drift]
    drift suite:
    |███▊ | 3/4 [Time: 00:03, Check=Label Drift]       
                                                         

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Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "934bdb0ac8d54fe48a73e1be526dc404": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3b1bcbf723e74f50b3b8d6e18f5a8999": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8b3215a7b04b4dafafacb6e3b516ad97": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6d4bde14177349d3aea54753433cd1dd", "IPY_MODEL_0028e6fee4494096a17a3f0ffc901bc3"], "layout": "IPY_MODEL_b20998a9b9f24b17b7f3a38a3d942996"}}, "6d4bde14177349d3aea54753433cd1dd": {"model_name": "HTMLModel", "model_module": 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"IPY_MODEL_d2bdfd6b39a5451a96728ddae69ff455", "selected_index": null}}, "c24af22926ec46f49fe9b4cec4aaee81": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_779c1ef73c9f442d85ba463143228dcf", "IPY_MODEL_0d908ae9b8954b6486dd963740a4eaac", "IPY_MODEL_95c2550eebc547379cae79637c8d1506", "IPY_MODEL_db9aefd89cbd4c6bbe7e34b5f32e9006", "IPY_MODEL_3e7819266bdb402ba5848f78698cf0df"], "layout": "IPY_MODEL_5802c67fe9774be681b1de7da087c657"}}, "779c1ef73c9f442d85ba463143228dcf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a3a8506111f4d81a513fc2528d891ff", "style": "IPY_MODEL_fea3d253734848629e35b9a21629647a", "value": "<style type=\"text/css\">\n#T_b6a2e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b6a2e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b6a2e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b6a2e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b6a2e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b6a2e_\">\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_b6a2e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b6a2e_row0_col1\" class=\"data row0 col1\" ><a href=\"#FeatureDrift_TJSWOSZNK4GVK5NH2IP9S8K4A-section-BSQ8U\">Feature Drift</a></td>\n      <td id=\"T_b6a2e_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_b6a2e_row0_col3\" class=\"data row0 col3\" >Failed for 2 out of 24 columns.\nFound 2 numeric columns with Kolmogorov-Smirnov above threshold: {'revol_util': '0.11', 'int_rate': '0.12'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_b6a2e_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b6a2e_row1_col1\" class=\"data row1 col1\" ><a href=\"#MultivariateDrift_TJSWOSZNK4GVK5NH2IP9S8K4A-section-BSQ8U\">Multivariate Drift</a></td>\n      <td id=\"T_b6a2e_row1_col2\" class=\"data row1 col2\" >Drift value is less than 0.4</td>\n      <td id=\"T_b6a2e_row1_col3\" class=\"data row1 col3\" >Found drift value of: 0.47, corresponding to a domain classifier AUC of: 0.73</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4a3a8506111f4d81a513fc2528d891ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fea3d253734848629e35b9a21629647a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d908ae9b8954b6486dd963740a4eaac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a49be2bb17fe41f7b58766cbee01f2ac", "style": "IPY_MODEL_269a26dc7911434087d5aa4b75219041", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a49be2bb17fe41f7b58766cbee01f2ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "269a26dc7911434087d5aa4b75219041": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "95c2550eebc547379cae79637c8d1506": {"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_3367260e9f7447009e188c331171a9ca", "IPY_MODEL_984b2692390946f39254d9ec14d04110", "IPY_MODEL_09c9db3a0d6946379eeba3e52baa2608", "IPY_MODEL_23805121fa1e403694b5c13a481ba3bf"], "layout": "IPY_MODEL_678de26c42124300965922065bd9162c"}}, "3367260e9f7447009e188c331171a9ca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11045e0d67f7475c8b8ce7b5556220b5", "style": "IPY_MODEL_56f69d7be333461b9b20c0aea5ab1ce4", "value": "<h4 id=\"FeatureDrift_TJSWOSZNK4GVK5NH2IP9S8K4A-section-BSQ8U\"><b>Feature Drift</b></h4>"}}, "11045e0d67f7475c8b8ce7b5556220b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "56f69d7be333461b9b20c0aea5ab1ce4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "984b2692390946f39254d9ec14d04110": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3626d1bd372149bb92d2ba67928776fb", "style": "IPY_MODEL_81805151efe84cd4854bbe898a7acedd", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3626d1bd372149bb92d2ba67928776fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81805151efe84cd4854bbe898a7acedd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "09c9db3a0d6946379eeba3e52baa2608": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_026182fc619b405a8a699369b7d89f33", "style": "IPY_MODEL_6e9e91bdb37747dab9f9c468e9f45e8f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_0b9b3_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b9b3_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b9b3_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b9b3_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0b9b3_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0b9b3_\">\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_0b9b3_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_0b9b3_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_0b9b3_row0_col2\" class=\"data row0 col2\" >Failed for 2 out of 24 columns.\nFound 2 numeric columns with Kolmogorov-Smirnov above threshold: {'revol_util': '0.11', 'int_rate': '0.12'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "026182fc619b405a8a699369b7d89f33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e9e91bdb37747dab9f9c468e9f45e8f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "23805121fa1e403694b5c13a481ba3bf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d251d50a7eb8491d9a976863b80dd795", "IPY_MODEL_12002c0015a14e00bdbc9dbd7e6f3314", "IPY_MODEL_d3d8e7e4431c4177b664240f2566bee9", "IPY_MODEL_cb3fc194baee40468ecc7998e1943b35", "IPY_MODEL_9f412ebdfba04baaba2fe82c6bc52bf9", "IPY_MODEL_decd9ba824bc43bdaf8b37fc264caf66", "IPY_MODEL_e092626146b146f4885cebc0e209eb73", "IPY_MODEL_dca56d79a2ca410aae582ec2532c9563", "IPY_MODEL_4a0c4b45fdc34922a6857489fabe692d", "IPY_MODEL_adc023fbe09e42098d317771be900f7c"], "layout": "IPY_MODEL_222484e4fbad4317b7b22d3caa89ef39"}}, "d251d50a7eb8491d9a976863b80dd795": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_829169d19e60419887ae63d7cc662a58", "style": "IPY_MODEL_3190887b6ef94e8aa56740aba1c55438", "value": "<h5><b>Additional Outputs</b></h5>"}}, "829169d19e60419887ae63d7cc662a58": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3190887b6ef94e8aa56740aba1c55438": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12002c0015a14e00bdbc9dbd7e6f3314": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c0c5eae0425a4a5e989204296d21243f", "style": "IPY_MODEL_cda96944532f4e8aa785ffca356aa584", "value": "<div>\n<span>\nThe Drift score is a measure for the difference between two distributions, in this check - the test\nand train distributions.<br> The check shows the drift score and distributions for the features,\nsorted by drift score and showing only the top 5 features,\naccording to drift score.\n</span>\n</div>"}}, "c0c5eae0425a4a5e989204296d21243f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cda96944532f4e8aa785ffca356aa584": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d3d8e7e4431c4177b664240f2566bee9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_79be60324a6e4d468276d2d32f95dd8d", "style": "IPY_MODEL_ec18e701d16f48e488288377c34e14b3", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "79be60324a6e4d468276d2d32f95dd8d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec18e701d16f48e488288377c34e14b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb3fc194baee40468ecc7998e1943b35": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_109d1e9bc3bf4449823e2b84e84d41b3", "style": "IPY_MODEL_918d1fbdcc3f471190816b61a1ee6a50", "value": "<div>If available, the plot titles also show the feature importance (FI) rank</div>"}}, "109d1e9bc3bf4449823e2b84e84d41b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "918d1fbdcc3f471190816b61a1ee6a50": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9f412ebdfba04baaba2fe82c6bc52bf9": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "b2d3e905-7872-4e38-836c-88fed2853eb3"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0375541803192031], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "745c878a-9ed9-4abc-8cd3-c0fb809a0b7b"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["A3", "A4", "B3", "C1", "C4", "C5", "D1", "D4", "D5", "E1"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "3bedbab0-7b90-4c77-a7b6-17ee761f8dc3"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": ["A3", "A4", "B3", "C1", "C4", "C5", "D1", "D4", "D5", "E1"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "2a42bcd8-cf69-4d05-b778-bbd5fa2f598e"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score 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"model_module_version": "1.2.0", "state": {}}, "db9aefd89cbd4c6bbe7e34b5f32e9006": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07957e24f0124590ab6132cdd84e1ff7", "style": "IPY_MODEL_dd41f80941ea4f01a903cd0f5137e799", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "07957e24f0124590ab6132cdd84e1ff7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd41f80941ea4f01a903cd0f5137e799": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3e7819266bdb402ba5848f78698cf0df": {"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_4bb41996fa3b42afb96bf02bd5dd4fa0", "IPY_MODEL_40c6bee6b1bb437d956bd30188a45bda", "IPY_MODEL_4276739627a24735a4b02925835080aa", "IPY_MODEL_a95c4fd86f634d079cf2f95acf85fb77"], "layout": "IPY_MODEL_bda5cbe2cd1a47ed88cb0f8139a7d6ae"}}, "4bb41996fa3b42afb96bf02bd5dd4fa0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7174f5b0d0364487a1cf5a37349eba88", "style": "IPY_MODEL_eaa50aad362143b48fba31e1eb2200e9", "value": "<h4 id=\"MultivariateDrift_TJSWOSZNK4GVK5NH2IP9S8K4A-section-BSQ8U\"><b>Multivariate Drift</b></h4>"}}, "7174f5b0d0364487a1cf5a37349eba88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eaa50aad362143b48fba31e1eb2200e9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40c6bee6b1bb437d956bd30188a45bda": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5e8925b843cd4d36bac613e07a4e59a7", "style": "IPY_MODEL_4bb0eb5111e545caaea73795f26519c1", "value": "<p>    Calculate drift between the entire train and test datasets using a model trained to distinguish between them. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_multivariate_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5e8925b843cd4d36bac613e07a4e59a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4bb0eb5111e545caaea73795f26519c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4276739627a24735a4b02925835080aa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cc1e5b48a8e7431c98e63ab0153a8405", "style": "IPY_MODEL_f825166808a8416c80b6bc92e06fd783", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_52604_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52604_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52604_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52604_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_52604_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_52604_\">\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_52604_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_52604_row0_col1\" class=\"data row0 col1\" >Drift value is less than 0.4</td>\n      <td id=\"T_52604_row0_col2\" class=\"data row0 col2\" >Found drift value of: 0.47, corresponding to a domain classifier AUC of: 0.73</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "cc1e5b48a8e7431c98e63ab0153a8405": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f825166808a8416c80b6bc92e06fd783": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a95c4fd86f634d079cf2f95acf85fb77": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8e39ae66665546199264d2d6075b2861", "IPY_MODEL_c71d96f21f7d4683ae172b4f2e779f84", "IPY_MODEL_b41331df584941ef94782bd730260303", "IPY_MODEL_ab3a7403ff3a4a96a66cdf942a9bcd05", "IPY_MODEL_075f21fa79a045aa89158e61efe9d37f", "IPY_MODEL_4631cba62b874c65a7d092f172de672f", "IPY_MODEL_574cfad517414ee582095a38bceb25a4", "IPY_MODEL_85cad534775d4ec0a236128ae8e52065", "IPY_MODEL_e166f52ce3a848ef96d0b0c00f08c533", "IPY_MODEL_965a67c7d6524922974c9867af5fcd31", "IPY_MODEL_9ace0e0d082545da898b0632b63e8428"], "layout": "IPY_MODEL_5bd812d5cd654940b2611658a7e60ac4"}}, "8e39ae66665546199264d2d6075b2861": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f64565237e63474cbaf431efd2cd8c69", "style": "IPY_MODEL_305e8a67b3494129b8f1283f11e9301b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f64565237e63474cbaf431efd2cd8c69": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "305e8a67b3494129b8f1283f11e9301b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c71d96f21f7d4683ae172b4f2e779f84": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b89ac3d682014ff2b23317e98628b201", "style": "IPY_MODEL_cc8b510aa9d6421aa48c5ef5c9284660", "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>"}}, "b89ac3d682014ff2b23317e98628b201": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc8b510aa9d6421aa48c5ef5c9284660": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b41331df584941ef94782bd730260303": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_315eefe303244506a285eb3b435c5b7e", "style": "IPY_MODEL_4451f7b0ba664a61bc484181851ea793", "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>"}}, "315eefe303244506a285eb3b435c5b7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4451f7b0ba664a61bc484181851ea793": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab3a7403ff3a4a96a66cdf942a9bcd05": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, 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{"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": "Drift Score - Multivariable"}, "xaxis": {"dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.5656852164808318], "showgrid": false, "title": {"text": "Drift score"}}, "yaxis": {"autorange": true, "color": "black", "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "075f21fa79a045aa89158e61efe9d37f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b2a8e08da1eb418c8484bd61b144f902", "style": "IPY_MODEL_7d29fd0e9a3f42a7b377de7042f96354", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "b2a8e08da1eb418c8484bd61b144f902": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d29fd0e9a3f42a7b377de7042f96354": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4631cba62b874c65a7d092f172de672f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_195496fa47c4436e923c3be7ba04add5", "style": "IPY_MODEL_5c119cf4a38e4823b0f0c0fb0eccdb0b", "value": "<div>Showing only the top 3 columns, you can change it using n_top_columns param</div>"}}, "195496fa47c4436e923c3be7ba04add5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5c119cf4a38e4823b0f0c0fb0eccdb0b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "574cfad517414ee582095a38bceb25a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fee1b5d0425446d7a8aecac6aac67531", "style": "IPY_MODEL_7dc18e6c18c84bb488d1061b1e680c70", "value": "<div>For discrete distribution plots, showing the top 10 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{"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "952a5ee057e84895bc6663bbcfbe6c87": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "84a2b1785ad24eeb88bed2fa155a773b": {"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_222de0fe2fd94c0ab864e71ca741eba0"], "layout": "IPY_MODEL_a70537a3a7d64903bc1087b80ae8e56b", "selected_index": null}}, "222de0fe2fd94c0ab864e71ca741eba0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_efe8c61fec5f4ccabf297a2c86ee1c09", "IPY_MODEL_a3015498682f42aabb5d4e6aaed9f97a", "IPY_MODEL_431085e37e1f493a9921999786cb50b3"], "layout": "IPY_MODEL_bccf21e667a54133a692355ee84e65f5"}}, "efe8c61fec5f4ccabf297a2c86ee1c09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e69bca8549364717bdc0065e71605e89", "style": "IPY_MODEL_1f73ac0ea81e4144a54f9d0a0186f46d", "value": "<style type=\"text/css\">\n#T_a90c7_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a90c7_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a90c7_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a90c7_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a90c7_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a90c7_\">\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_a90c7_row0_col0\" class=\"data row0 col0\" ><a href=\"#LabelDrift_TJSWOSZNK4GVK5NH2IP9S8K4A-section-CG02V\">Label Drift</a></td>\n      <td id=\"T_a90c7_row0_col1\" class=\"data row0 col1\" >    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e69bca8549364717bdc0065e71605e89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f73ac0ea81e4144a54f9d0a0186f46d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a3015498682f42aabb5d4e6aaed9f97a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3fac08be58a481eae1cc2c0b24a6f5f", "style": "IPY_MODEL_911d0b70006c4cccb9bad45b78f03da8", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b3fac08be58a481eae1cc2c0b24a6f5f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "911d0b70006c4cccb9bad45b78f03da8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "431085e37e1f493a9921999786cb50b3": {"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_13797df0e62540889244b1a3381bcf57", "IPY_MODEL_ac0f3c1a79b647fbaf7720fcf73217ff", "IPY_MODEL_f42df47309984cc48a7d0ae66ebd525e"], "layout": "IPY_MODEL_ae19bd926afb4665b8738c9ac3703434"}}, "13797df0e62540889244b1a3381bcf57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f040fb5e998f49a1a37777a9fa5f4415", "style": "IPY_MODEL_3e105f8c37f444eea9aa010d1dfb07e9", "value": "<h4 id=\"LabelDrift_TJSWOSZNK4GVK5NH2IP9S8K4A-section-CG02V\"><b>Label Drift</b></h4>"}}, "f040fb5e998f49a1a37777a9fa5f4415": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e105f8c37f444eea9aa010d1dfb07e9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ac0f3c1a79b647fbaf7720fcf73217ff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b878e2b5b82e4770b9c4c3e72fef851d", "style": "IPY_MODEL_7f1d4b77a56b428db7f45fec1d2562d5", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/tabular/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b878e2b5b82e4770b9c4c3e72fef851d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7f1d4b77a56b428db7f45fec1d2562d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f42df47309984cc48a7d0ae66ebd525e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1e89679b4a904b59b0def7277ccbabc2", "IPY_MODEL_6372914ee61e4a0fad08f5825976253e", "IPY_MODEL_f66e83e0f1d54bff941db8662c5ca10c", "IPY_MODEL_fdfe3f1f1dad47a08526132ae0fa3d90", "IPY_MODEL_f3a62342243c47a78e66f9b85a830bd8"], "layout": "IPY_MODEL_e4b98d8748cf44b7a6bfa7400afcb7b3"}}, "1e89679b4a904b59b0def7277ccbabc2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_15d161f680424410b64b64c2f8d04356", "style": "IPY_MODEL_8bad48a7104c4ba6aa08fd7f0df08ed0", "value": "<h5><b>Additional Outputs</b></h5>"}}, "15d161f680424410b64b64c2f8d04356": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8bad48a7104c4ba6aa08fd7f0df08ed0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6372914ee61e4a0fad08f5825976253e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1656ee8828cf437e882da876b59f3041", "style": "IPY_MODEL_3a7a54ba2bf4472bbca42fbc189de0cf", "value": "<div><span>\n                        The Drift score is a measure for the difference between two distributions, in this check -\n                        the test and train distributions.<br> The check shows the drift score\n                        and distributions for the label. </span></div>"}}, "1656ee8828cf437e882da876b59f3041": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a7a54ba2bf4472bbca42fbc189de0cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f66e83e0f1d54bff941db8662c5ca10c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_634d8dcab1d24ad6939d79dfb45d58ef", "style": "IPY_MODEL_0ea323b831de4510811619278ae9055e", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "634d8dcab1d24ad6939d79dfb45d58ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0ea323b831de4510811619278ae9055e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fdfe3f1f1dad47a08526132ae0fa3d90": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.17.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.018721898797421657], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "e2eae734-0dc8-415b-b2e2-65d11be01575"}, {"marker": {"color": 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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 21.347 seconds)


.. _sphx_glr_download_tabular_auto_tutorials_quickstarts_plot_quick_train_test_validation.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_quick_train_test_validation.py <plot_quick_train_test_validation.py>`

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

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


.. only:: html

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

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