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

.. only:: html

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

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

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

.. _sphx_glr_user-guide_general_exporting_results_examples_plot_export_suite_results_as_html.py:


Export Suite Output to a HTML Report
************************************

In this guide, we will demonstrate how to export a suite's output as an
HTML report. This enables easily sharing the results easier and also
using deepchecks outside of the notebook environment.

**Structure:**

* `Save Suite Result to an HTML Report <#save-suite-result-to-an-html-report>`__

.. GENERATED FROM PYTHON SOURCE LINES 16-19

Load Data
---------
Let's fetch the iris train and test datasets

.. GENERATED FROM PYTHON SOURCE LINES 19-24

.. code-block:: default


    from deepchecks.tabular.datasets.classification import iris

    train_dataset, test_dataset = iris.load_data()








.. GENERATED FROM PYTHON SOURCE LINES 25-27

Run Suite
---------

.. GENERATED FROM PYTHON SOURCE LINES 27-32

.. code-block:: default


    from deepchecks.tabular.suites import full_suite

    suite = full_suite()








.. GENERATED FROM PYTHON SOURCE LINES 33-36

.. code-block:: default


    suite_result = suite.run(train_dataset=train_dataset, test_dataset=test_dataset)





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

 .. code-block:: none


    Full Suite:
    |                                    | 0/36 [Time: 00:00]
    Full Suite:
    |#####################               | 21/36 [Time: 00:00, Check=Feature Label Correlation Change]
                                                                                                              



.. GENERATED FROM PYTHON SOURCE LINES 37-42

Save Suite Result to an HTML Report
===================================
Exporting the suite's output to an HTML file is possible using the ``save_as_html``
function. This function expects a file-like object, whether it's a file name or
the full path to the destination folder.

.. GENERATED FROM PYTHON SOURCE LINES 42-48

.. code-block:: default


    suite_result.save_as_html('my_suite.html')

    # or
    suite_result.save_as_html() # will save the result in output.html





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

 .. code-block:: none


    'output.html'



.. GENERATED FROM PYTHON SOURCE LINES 49-56

.. code-block:: default


    # Removing outputs created. this cell should be hidden in nbpshinx using "nbsphinx: hidden" in the metadata
    import os

    os.remove('output.html')
    os.remove('my_suite.html')








.. GENERATED FROM PYTHON SOURCE LINES 57-61

**Working with in-memory buffers**

The suite output can also be written into a file buffers. This can be done by
setting the file argument with a ``StringIO`` or ``BytesIO`` buffer object.

.. GENERATED FROM PYTHON SOURCE LINES 61-67

.. code-block:: default


    import io

    html_out = io.StringIO()
    suite_result.save_as_html(file=html_out)








.. GENERATED FROM PYTHON SOURCE LINES 68-71

View Suite Output
-----------------
The suite's output can still be viewed within the notebook

.. GENERATED FROM PYTHON SOURCE LINES 71-73

.. code-block:: default


    suite_result





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <!--
        ~ ----------------------------------------------------------------------------
        ~ Copyright (C) 2021-2022 Deepchecks (https://www.deepchecks.com)
        ~
        ~ This file is part of Deepchecks.
        ~ Deepchecks is distributed under the terms of the GNU Affero General
        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
        ~ along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
        ~ ----------------------------------------------------------------------------
        ~
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Read more about\n                <a href=https://docs.deepchecks.com/en/stable/examples/guides/create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n            </p>\n\n"}}, "e4ce70dd2f934dd082b34d59688cbdb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0ed93fd9e8504b6fa3779df9acc81316": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2448a6bbb444c6f8c0b0e7f9e34c7b3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6e414c00e6de442883572c5968255812", "IPY_MODEL_7c03040d79ee467f9176f156e244d023"], "layout": "IPY_MODEL_3c28414e6a1d476daf71f339795bd456"}}, "6e414c00e6de442883572c5968255812": {"model_name": "HTMLModel", "model_module": 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"IPY_MODEL_c3dc2b53c9b0475dbca3f23e725e0d6f", "selected_index": null}}, "b935bfe1b5ae4fef88d1de20542bf92a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_37e0ab9c30224bcaae867c366399fb44", "IPY_MODEL_8e060c78ce964b34a4093b621e28e85c", "IPY_MODEL_ee00957649994c89bf78a54000c1917a", "IPY_MODEL_e1ab65431fa04932b7306a5f689d46c5", "IPY_MODEL_026a99da89eb45d7a09d8b38d24736f5", "IPY_MODEL_e1ab65431fa04932b7306a5f689d46c5", "IPY_MODEL_4b803c821a384139889076e3034b327e", "IPY_MODEL_e1ab65431fa04932b7306a5f689d46c5", "IPY_MODEL_6586c69e61dc4eb5a61a59204dee9313", "IPY_MODEL_e1ab65431fa04932b7306a5f689d46c5", "IPY_MODEL_53eeeb1b3f3f46c5b8872ee89ab2dd6f"], "layout": "IPY_MODEL_0e62f5ef7da54042a0c446409cde3b58"}}, "37e0ab9c30224bcaae867c366399fb44": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a8188af32804db9a937b8ece9719335", "style": "IPY_MODEL_14d2f77c4f51477b991f793def8b5cb2", "value": "<style type=\"text/css\">\n#T_92c45_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_92c45_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_92c45_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_92c45_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_92c45_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_92c45_\">\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_92c45_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_92c45_row0_col1\" class=\"data row0 col1\" ><a href=\"#FeatureLabelCorrelationChange_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\">Feature Label Correlation Change</a></td>\n      <td id=\"T_92c45_row0_col2\" class=\"data row0 col2\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_92c45_row0_col3\" class=\"data row0 col3\" >Found 2 out of 4 features in train dataset with PPS above threshold: {'petal width (cm)': '0.91', 'petal length (cm)': '0.84'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_92c45_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_92c45_row1_col1\" class=\"data row1 col1\" ><a href=\"#FeatureLabelCorrelation-TrainDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\">Feature Label Correlation - Train Dataset</a></td>\n      <td id=\"T_92c45_row1_col2\" class=\"data row1 col2\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_92c45_row1_col3\" class=\"data row1 col3\" >Found 2 out of 4 features with PPS above threshold: {'petal width (cm)': '0.91', 'petal length (cm)': '0.84'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_92c45_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_92c45_row2_col1\" class=\"data row2 col1\" ><a href=\"#FeatureLabelCorrelation-TestDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\">Feature Label Correlation - Test Dataset</a></td>\n      <td id=\"T_92c45_row2_col2\" class=\"data row2 col2\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_92c45_row2_col3\" class=\"data row2 col3\" >Found 2 out of 4 features with PPS above threshold: {'petal length (cm)': '1', 'petal width (cm)': '0.84'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_92c45_row3_col0\" class=\"data row3 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_92c45_row3_col1\" class=\"data row3 col1\" ><a href=\"#Feature-FeatureCorrelation-TrainDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\">Feature-Feature Correlation - Train Dataset</a></td>\n      <td id=\"T_92c45_row3_col2\" class=\"data row3 col2\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_92c45_row3_col3\" class=\"data row3 col3\" >Correlation is greater than 0.9 for pairs [('petal length (cm)', 'sepal length (cm)'), ('petal length (cm)', 'petal width (cm)')]</td>\n    </tr>\n    <tr>\n      <td id=\"T_92c45_row4_col0\" class=\"data row4 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_92c45_row4_col1\" class=\"data row4 col1\" ><a href=\"#Feature-FeatureCorrelation-TestDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\">Feature-Feature Correlation - Test Dataset</a></td>\n      <td id=\"T_92c45_row4_col2\" class=\"data row4 col2\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_92c45_row4_col3\" class=\"data row4 col3\" >Correlation is greater than 0.9 for pairs [('petal length (cm)', 'petal width (cm)')]</td>\n    </tr>\n    <tr>\n      <td id=\"T_92c45_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_92c45_row5_col1\" class=\"data row5 col1\" ><a href=\"#FeatureLabelCorrelationChange_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\">Feature Label Correlation Change</a></td>\n      <td id=\"T_92c45_row5_col2\" class=\"data row5 col2\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_92c45_row5_col3\" class=\"data row5 col3\" >Passed for 4 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3a8188af32804db9a937b8ece9719335": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14d2f77c4f51477b991f793def8b5cb2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e060c78ce964b34a4093b621e28e85c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_890b11dd584e4baca9bd08d1159376dc", "style": "IPY_MODEL_95448063cc4c4e55bdbc4657a1929213", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "890b11dd584e4baca9bd08d1159376dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95448063cc4c4e55bdbc4657a1929213": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee00957649994c89bf78a54000c1917a": {"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_9f7470b2e0b848bb8ba68461fc314bde", "IPY_MODEL_d3bde6c1c24247bca752c6290c641c0c", "IPY_MODEL_b6772ed39a7e4abca2440baa5874bdd7", "IPY_MODEL_17391b4449374057851f7d4d20ad91a2"], "layout": "IPY_MODEL_1222568fb97f4e44a9af93d86cfce5b9"}}, "9f7470b2e0b848bb8ba68461fc314bde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b22276d74824348ae191f2bc9cb97dc", "style": "IPY_MODEL_6bd413433b5b41fba10349d57c5a3162", "value": "<h4 id=\"FeatureLabelCorrelationChange_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\"><b>Feature Label Correlation Change</b></h4>"}}, "1b22276d74824348ae191f2bc9cb97dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6bd413433b5b41fba10349d57c5a3162": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d3bde6c1c24247bca752c6290c641c0c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_280dff36434448abb2182833ce79237b", "style": "IPY_MODEL_eebfb3dab056428d93e7ffcf6b0c6919", "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/0.8/checks_gallery/tabular/train_test_validation/plot_feature_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "280dff36434448abb2182833ce79237b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eebfb3dab056428d93e7ffcf6b0c6919": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b6772ed39a7e4abca2440baa5874bdd7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9974a098554c496783d0b7674149cfbb", "style": "IPY_MODEL_db23d229119b436d9ab8ee0006ab8068", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f4f4b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4f4b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4f4b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4f4b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f4f4b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f4f4b_\">\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_f4f4b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f4f4b_row0_col1\" class=\"data row0 col1\" >Train features' Predictive Power Score is less than 0.7</td>\n      <td id=\"T_f4f4b_row0_col2\" class=\"data row0 col2\" >Found 2 out of 4 features in train dataset with PPS above threshold: {'petal width (cm)': '0.91', 'petal length (cm)': '0.84'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_f4f4b_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f4f4b_row1_col1\" class=\"data row1 col1\" >Train-Test features' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_f4f4b_row1_col2\" class=\"data row1 col2\" >Passed for 4 relevant columns</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9974a098554c496783d0b7674149cfbb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db23d229119b436d9ab8ee0006ab8068": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17391b4449374057851f7d4d20ad91a2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_45dbb2ff120b4b6b9c7c243fe22e8301", "IPY_MODEL_e9ae9572bb364121b9d116f6646c8566", "IPY_MODEL_e66dafcc59444c9ca2ac33f8526a9089", "IPY_MODEL_cb0fe5edd15c44e6b2cece37428ac332", "IPY_MODEL_041bf355848a492da6bf8efe54198479", "IPY_MODEL_972c7e737c444886bab11b74c53438d8", "IPY_MODEL_f9f38142bd624f98ba35405b7578b157", "IPY_MODEL_b42a27064d564250b102e5c15f5c75d6", "IPY_MODEL_d826a5e4837d4540824f8255db17ec66", "IPY_MODEL_2be6a3d88b95497e97c887e053438969", "IPY_MODEL_cdfaab2e5fdb4652be9f8d197f4761c6", "IPY_MODEL_81dc65c18e764aa1abdc24f6c7c93b6d", "IPY_MODEL_58688048d89e4e25a584a87cf76e36b3"], "layout": "IPY_MODEL_48c03d2a2b67463982b80b46b9b357a5"}}, "45dbb2ff120b4b6b9c7c243fe22e8301": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9007b52e9b0d47fb98f04556e6426ab7", "style": "IPY_MODEL_e48c4ef50d1644c08c1439a895f064eb", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9007b52e9b0d47fb98f04556e6426ab7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e48c4ef50d1644c08c1439a895f064eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9ae9572bb364121b9d116f6646c8566": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.84</b>", "<b>0.09</b>", "<b>0.91</b>", "<b>0.41</b>"], "textposition": "outside", "x": ["petal length (cm)", "sepal width (cm)", "petal width (cm)", "sepal length (cm)"], "y": {"dtype": "float64", "shape": [4]}, "type": "bar", "uid": "bf96ee82-2228-4221-ac9e-928e14c4c2ad"}, {"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>(+16%)<br>1.0</b>", "<b>(-9%)<br>0.0</b>", "<b>(-7%)<br>0.84</b>", "<b>(-5%)<br>0.36</b>"], "textposition": "outside", "x": ["petal length (cm)", "sepal width (cm)", "petal width (cm)", "sepal length (cm)"], "y": {"dtype": "float64", "shape": [4]}, "type": "bar", "uid": "2c8b768f-f1d4-44d0-9a7c-42a62cf1dc51"}], "_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, 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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": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": 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[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, 6], "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": "4XoUrkfh6j+IAO4rcSu2PwSHtIwPPe0/YrW1h3QO2j8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAA8D8AAAAAAAAAALwoyZyj3eo/EAGuFjsb1z8="}]}, "e66dafcc59444c9ca2ac33f8526a9089": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_85c38005d2074ecfbfdde781e99bb7dc", "style": "IPY_MODEL_b0ddf1bec87b4d6695d4b0dc13c28007", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/en/stable/checks_gallery/tabular/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "85c38005d2074ecfbfdde781e99bb7dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b0ddf1bec87b4d6695d4b0dc13c28007": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb0fe5edd15c44e6b2cece37428ac332": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cbe6f552d76b4a8286f94cd93e47f3dd", "style": "IPY_MODEL_f05efed405ed42388324758f207fa026", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "cbe6f552d76b4a8286f94cd93e47f3dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f05efed405ed42388324758f207fa026": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "041bf355848a492da6bf8efe54198479": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_429be4a6428b455380872924a028b3c4", "style": "IPY_MODEL_5e61ac3803b2437ca44bd686f572d34f", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "429be4a6428b455380872924a028b3c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e61ac3803b2437ca44bd686f572d34f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "972c7e737c444886bab11b74c53438d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_71b521a6918a481ca70947ed1d33b7e6", "style": "IPY_MODEL_998339d2022b478498d6351822f7f0b9", "value": "<div>Can indicate that this feature's success in predicting the label is actually due to data leakage, </div>"}}, "71b521a6918a481ca70947ed1d33b7e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "998339d2022b478498d6351822f7f0b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f9f38142bd624f98ba35405b7578b157": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_77129c0c9c634285afd2b525e18029c0", "style": "IPY_MODEL_531f0bc2cbf34299b2ba8e78fb9ee67c", "value": "<div>   meaning that the feature holds information that is based on the label to begin with.</div>"}}, "77129c0c9c634285afd2b525e18029c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "531f0bc2cbf34299b2ba8e78fb9ee67c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b42a27064d564250b102e5c15f5c75d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6487c84922a640c38e4108b7be44fb13", "style": "IPY_MODEL_5930e9574c694d5a8c84281096453be2", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "6487c84922a640c38e4108b7be44fb13": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5930e9574c694d5a8c84281096453be2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d826a5e4837d4540824f8255db17ec66": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fddf07279aec47a098346c5d5fc5bbb7", "style": "IPY_MODEL_22a1d8a734484226b9b2266406071d47", "value": "<div>   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test </div>"}}, "fddf07279aec47a098346c5d5fc5bbb7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "22a1d8a734484226b9b2266406071d47": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2be6a3d88b95497e97c887e053438969": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e1fd0a6b91d441e9aa98090874bd253", "style": "IPY_MODEL_47b3fbace6304ffd95393899e3ed9515", "value": "<div>   can be explained by leakage in train that is not relevant to a new dataset.</div>"}}, "4e1fd0a6b91d441e9aa98090874bd253": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "47b3fbace6304ffd95393899e3ed9515": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cdfaab2e5fdb4652be9f8d197f4761c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9d37030881e84fedbe6b4dab72cd458c", "style": "IPY_MODEL_d539fd1bff214959866932d9de5903df", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "9d37030881e84fedbe6b4dab72cd458c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d539fd1bff214959866932d9de5903df": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "81dc65c18e764aa1abdc24f6c7c93b6d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1602669fdec9485f943e32782610f97c", "style": "IPY_MODEL_e8620fd685ed44d9a298fb2053b320f0", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "1602669fdec9485f943e32782610f97c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e8620fd685ed44d9a298fb2053b320f0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58688048d89e4e25a584a87cf76e36b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6e4d6b249f0b4e7ba1df929d6fe1a648", "style": "IPY_MODEL_27bc94e88af740e69596d8dbedb29afa", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\" style=\"font-size: 14px;\">Go to top</a>"}}, "6e4d6b249f0b4e7ba1df929d6fe1a648": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "27bc94e88af740e69596d8dbedb29afa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "48c03d2a2b67463982b80b46b9b357a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1222568fb97f4e44a9af93d86cfce5b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e1ab65431fa04932b7306a5f689d46c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4df28392bee54281bc63e8cf25ea8b9a", "style": "IPY_MODEL_5002b4d27c674c6594db87e3edb1530e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "4df28392bee54281bc63e8cf25ea8b9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5002b4d27c674c6594db87e3edb1530e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "026a99da89eb45d7a09d8b38d24736f5": {"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_7126a7d43e82407b8773026531ab34ce", "IPY_MODEL_c024b704db1b44928f802d29a82785bb", "IPY_MODEL_90340e97fdbb4d73a856f1dff3cfa81b", "IPY_MODEL_d5e8bfc07f35497b92cf9d8f7d1a0fbb"], "layout": "IPY_MODEL_59a57043f3484c8d999ff135ac691c85"}}, "7126a7d43e82407b8773026531ab34ce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ead63de521354057ab89047e759e3cc6", "style": "IPY_MODEL_71d75b20c34243ecb2319bb85b632cdd", "value": "<h4 id=\"FeatureLabelCorrelation-TrainDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\"><b>Feature Label Correlation - Train Dataset</b></h4>"}}, "ead63de521354057ab89047e759e3cc6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71d75b20c34243ecb2319bb85b632cdd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c024b704db1b44928f802d29a82785bb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_94008c0098a64c8a9d822045789263bf", "style": "IPY_MODEL_da254670ce174609967789ff632691d0", "value": "<p>Return the PPS (Predictive Power Score) of all features in relation to the label. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/data_integrity/plot_feature_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "94008c0098a64c8a9d822045789263bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da254670ce174609967789ff632691d0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "90340e97fdbb4d73a856f1dff3cfa81b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_98c394d75c764df881462bf66160884b", "style": "IPY_MODEL_b1c5c74569db4d9bac8e9456d9fb45ea", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_17dde_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17dde_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17dde_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17dde_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17dde_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_17dde_\">\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_17dde_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_17dde_row0_col1\" class=\"data row0 col1\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_17dde_row0_col2\" class=\"data row0 col2\" >Found 2 out of 4 features with PPS above threshold: {'petal width (cm)': '0.91', 'petal length (cm)': '0.84'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "98c394d75c764df881462bf66160884b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1c5c74569db4d9bac8e9456d9fb45ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d5e8bfc07f35497b92cf9d8f7d1a0fbb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4699835899c045109d0f2c4fd464349b", "IPY_MODEL_f44ead5b9ff44e90b45f19435b421bcb", "IPY_MODEL_f2c1c19dd298418a92d65d579f791f5a", "IPY_MODEL_0f62aed8d9a34c45a30a6e822d49e9b4"], "layout": "IPY_MODEL_ee0842eb8fe546cca3fec2373d414fb9"}}, "4699835899c045109d0f2c4fd464349b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_480a5384ad3e442fae696d3116c923bb", "style": "IPY_MODEL_43030ddfab3e461091f22b6200f501af", "value": "<h5><b>Additional Outputs</b></h5>"}}, "480a5384ad3e442fae696d3116c923bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "43030ddfab3e461091f22b6200f501af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f44ead5b9ff44e90b45f19435b421bcb": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.91</b>", "<b>0.84</b>", "<b>0.41</b>", "<b>0.09</b>"], "textposition": "outside", "x": ["petal width (cm)", "petal length (cm)", "sepal length (cm)", "sepal width (cm)"], "y": {"dtype": "float64", "shape": [4]}, "type": "bar", "uid": "090ec109-7162-4d79-bf79-7d75897d5bd1"}], "_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"], 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"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, 6], "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": "BIe0jA897T/hehSuR+HqP2K1tYd0Dto/iADuK3Ertj8="}]}, "f2c1c19dd298418a92d65d579f791f5a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_284512f108e8465d8f8addbb9fae6358", "style": "IPY_MODEL_2a63175edcab45fdab1ab08418811acb", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself (Read more about <a href=https://docs.deepchecks.com/en/stable/checks_gallery/tabular/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>). A high PPS (close to 1) can mean that this feature's success in predicting the label is actually due to data leakage - meaning that the feature holds information that is based on the label to begin with.</div>"}}, "284512f108e8465d8f8addbb9fae6358": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a63175edcab45fdab1ab08418811acb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f62aed8d9a34c45a30a6e822d49e9b4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5ffd8caefb1445cabc0b8e1a5959169", "style": "IPY_MODEL_9bded00c9696426baa6e75397ef70756", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\" style=\"font-size: 14px;\">Go to top</a>"}}, "a5ffd8caefb1445cabc0b8e1a5959169": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9bded00c9696426baa6e75397ef70756": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee0842eb8fe546cca3fec2373d414fb9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59a57043f3484c8d999ff135ac691c85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b803c821a384139889076e3034b327e": {"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_8b282e46b648441c8d448e99b00300eb", "IPY_MODEL_f93ad85ae33f41c092c8c60e989127b3", "IPY_MODEL_28a515a7ff244e67b6f4fdaf20d95e9d", "IPY_MODEL_e04ef828661f4fd0ae489ec05f5a0780"], "layout": "IPY_MODEL_09af15319d334986bb797fbd57bfb07d"}}, "8b282e46b648441c8d448e99b00300eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_05c5df409ac248dca9dc7e06045ae795", "style": "IPY_MODEL_dc01601b7b464edaa46620fc4535d6cf", "value": "<h4 id=\"FeatureLabelCorrelation-TestDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\"><b>Feature Label Correlation - Test Dataset</b></h4>"}}, "05c5df409ac248dca9dc7e06045ae795": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc01601b7b464edaa46620fc4535d6cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f93ad85ae33f41c092c8c60e989127b3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c07638ca4a44283831b456dcbdb7d27", "style": "IPY_MODEL_e13ec3b1e4604a4db874ebbc99423acf", "value": "<p>Return the PPS (Predictive Power Score) of all features in relation to the label. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/data_integrity/plot_feature_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5c07638ca4a44283831b456dcbdb7d27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e13ec3b1e4604a4db874ebbc99423acf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "28a515a7ff244e67b6f4fdaf20d95e9d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bef8cda40e854046805d9e66c58e4298", "style": "IPY_MODEL_c87d11843b964663b3a5c17bd2eadd67", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_2ac9e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2ac9e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2ac9e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2ac9e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2ac9e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2ac9e_\">\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_2ac9e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2ac9e_row0_col1\" class=\"data row0 col1\" >Features' Predictive Power Score is less than 0.8</td>\n      <td id=\"T_2ac9e_row0_col2\" class=\"data row0 col2\" >Found 2 out of 4 features with PPS above threshold: {'petal length (cm)': '1', 'petal width (cm)': '0.84'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bef8cda40e854046805d9e66c58e4298": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c87d11843b964663b3a5c17bd2eadd67": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e04ef828661f4fd0ae489ec05f5a0780": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_55e3f3bf73fa49239726ed3b7aee2483", "IPY_MODEL_2cf66aeaa824492c97697ca96678dde2", "IPY_MODEL_83aa5a264d8841aebf7219337d28e995", "IPY_MODEL_0f4038efcb2d40c5b229a61b0a721278"], "layout": "IPY_MODEL_bb0b88c75e5c4c01ba978ceb74b4541a"}}, 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 6], "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": "AAAAAAAA8D+8KMmco93qPxABrhY7G9c/AAAAAAAAAAA="}]}, "83aa5a264d8841aebf7219337d28e995": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_99e0d0ce9bef41a6bd01daf43c535be1", "style": "IPY_MODEL_06ca2e37bd6a4c9abb351b441da3dc20", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself (Read more about <a href=https://docs.deepchecks.com/en/stable/checks_gallery/tabular/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>). A high PPS (close to 1) can mean that this feature's success in predicting the label is actually due to data leakage - meaning that the feature holds information that is based on the label to begin with.</div>"}}, "99e0d0ce9bef41a6bd01daf43c535be1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "06ca2e37bd6a4c9abb351b441da3dc20": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f4038efcb2d40c5b229a61b0a721278": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb57a8d6fe5142fc844ad53e3b359f8d", "style": "IPY_MODEL_d7eaaaa8fdc34dfdb975649b4bacaa20", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\" style=\"font-size: 14px;\">Go to top</a>"}}, "fb57a8d6fe5142fc844ad53e3b359f8d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d7eaaaa8fdc34dfdb975649b4bacaa20": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb0b88c75e5c4c01ba978ceb74b4541a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09af15319d334986bb797fbd57bfb07d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6586c69e61dc4eb5a61a59204dee9313": {"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_5266efcb205e4d7fabaeb0c38c77380e", "IPY_MODEL_aeadae80c93248b0a795bfa8658ba9fc", "IPY_MODEL_59ebd659384b454b97e63b512747e90b", "IPY_MODEL_145ad361c8fb40688e31b495e0fcd682"], "layout": "IPY_MODEL_ca56727fd43f430e8809993ac9acdc9f"}}, "5266efcb205e4d7fabaeb0c38c77380e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2fe198a19d644744b2730dd51ee1a170", "style": "IPY_MODEL_63400027a9194cfe8f2c3d7c0c3f099b", "value": "<h4 id=\"Feature-FeatureCorrelation-TrainDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\"><b>Feature-Feature Correlation - Train Dataset</b></h4>"}}, "2fe198a19d644744b2730dd51ee1a170": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "63400027a9194cfe8f2c3d7c0c3f099b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aeadae80c93248b0a795bfa8658ba9fc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9ff245a0f5e04201b86fff98353c8adc", "style": "IPY_MODEL_98d599a6400b437ea70dde9445103688", "value": "<p>    Checks for pairwise correlation between the features. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/data_integrity/plot_feature_feature_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9ff245a0f5e04201b86fff98353c8adc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "98d599a6400b437ea70dde9445103688": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59ebd659384b454b97e63b512747e90b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e96cf217d6724acf88dee3c13f0bfbd3", "style": "IPY_MODEL_fe50fe60fed045409eccce0f301309fc", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5c6d0_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5c6d0_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5c6d0_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5c6d0_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5c6d0_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5c6d0_\">\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_5c6d0_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_5c6d0_row0_col1\" class=\"data row0 col1\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_5c6d0_row0_col2\" class=\"data row0 col2\" >Correlation is greater than 0.9 for pairs [('petal length (cm)', 'sepal length (cm)'), ('petal length (cm)', 'petal width (cm)')]</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e96cf217d6724acf88dee3c13f0bfbd3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe50fe60fed045409eccce0f301309fc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "145ad361c8fb40688e31b495e0fcd682": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_15907b3c4245497d9dd5af8a4ad2a71c", "IPY_MODEL_ec1a7b17ef224cf9aa716f1053d02bcb", "IPY_MODEL_9994d1bbba0048bab1d6865918823997", "IPY_MODEL_5b6c01add79948d9abbb81c396e78e86"], "layout": "IPY_MODEL_863e29c3b75348b1bc351a03d21f04c9"}}, "15907b3c4245497d9dd5af8a4ad2a71c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c9168860077647cd9963094b0a2e9818", "style": "IPY_MODEL_3551690965c743b9bbaaa68d29a36438", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c9168860077647cd9963094b0a2e9818": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3551690965c743b9bbaaa68d29a36438": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec1a7b17ef224cf9aa716f1053d02bcb": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hovertemplate": "x: %{x}<br>y: %{y}<br>color: 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"autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0]}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "9994d1bbba0048bab1d6865918823997": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_52e6b43e3bfe4a8eab41b4c599324cc2", "style": "IPY_MODEL_59fed8b869bb460a96683288c8e3dd7e", "value": "<div>* Displayed as absolute values.</div>"}}, "52e6b43e3bfe4a8eab41b4c599324cc2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59fed8b869bb460a96683288c8e3dd7e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5b6c01add79948d9abbb81c396e78e86": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a8f694188cb443d48b6c68b5e944de53", "style": "IPY_MODEL_ce02b40951254c68b3d69015055313eb", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\" style=\"font-size: 14px;\">Go to top</a>"}}, "a8f694188cb443d48b6c68b5e944de53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ce02b40951254c68b3d69015055313eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "863e29c3b75348b1bc351a03d21f04c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ca56727fd43f430e8809993ac9acdc9f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "53eeeb1b3f3f46c5b8872ee89ab2dd6f": {"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_cd2d1cf5d43b417d8c55d14e41964f18", "IPY_MODEL_1846802a48e54cb5b6d83c9aea9eae7b", "IPY_MODEL_7f4af48d777f4fcca9814e0df15a6dc8", "IPY_MODEL_d9d2728ecd9d427095b39c93441f4535"], "layout": "IPY_MODEL_f9a8190967eb454c8b013669237cb25a"}}, "cd2d1cf5d43b417d8c55d14e41964f18": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f73a98cbfce6423ebc11e2ac8b44e7d9", "style": "IPY_MODEL_f8ed03d7add448eb9c0742fb6dfee890", "value": "<h4 id=\"Feature-FeatureCorrelation-TestDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-4UOV5\"><b>Feature-Feature Correlation - Test Dataset</b></h4>"}}, "f73a98cbfce6423ebc11e2ac8b44e7d9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f8ed03d7add448eb9c0742fb6dfee890": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1846802a48e54cb5b6d83c9aea9eae7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_980cb26676d54d7691cdadfd06698bd2", "style": "IPY_MODEL_ae50cdbbc57e4f8cad20b6a57bcd8113", "value": "<p>    Checks for pairwise correlation between the features. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/data_integrity/plot_feature_feature_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "980cb26676d54d7691cdadfd06698bd2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae50cdbbc57e4f8cad20b6a57bcd8113": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f4af48d777f4fcca9814e0df15a6dc8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_486ea7b515e94be7ae091793851d7b02", "style": "IPY_MODEL_2db49158a06c468eb87088ee548b96e5", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_2639e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2639e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2639e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2639e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2639e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2639e_\">\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_2639e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2639e_row0_col1\" class=\"data row0 col1\" >Not more than 0 pairs are correlated above 0.9</td>\n      <td id=\"T_2639e_row0_col2\" class=\"data row0 col2\" >Correlation is greater than 0.9 for pairs [('petal length (cm)', 'petal width (cm)')]</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "486ea7b515e94be7ae091793851d7b02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2db49158a06c468eb87088ee548b96e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, 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"linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y"}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0]}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "4cd29f52b40047e2bfffbc0f41c9986c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8765084f8ba345d6a4ae41e076aba3b7", "style": "IPY_MODEL_d4d893bfda714cf981d51a8fc2589906", "value": 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"model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_655fba65b32b4fcc8fca45d0d409e0ce", "IPY_MODEL_da01ef53a7144e05b8fad237a9acf5de"], "layout": "IPY_MODEL_2819ce34655d4e73a810cd083fbb3f52"}}, "655fba65b32b4fcc8fca45d0d409e0ce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f3c85de1e27742a7b68926e091f33055", "style": "IPY_MODEL_64603b81fbd04528b67c8ba533654230", "value": "<span id=\"summary_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\"></span>"}}, "f3c85de1e27742a7b68926e091f33055": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64603b81fbd04528b67c8ba533654230": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da01ef53a7144e05b8fad237a9acf5de": {"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_5230ba1b0dd24240b06cca46b8007177"], "layout": "IPY_MODEL_4b11d843eecb4744bb7cdbef9c77d294", "selected_index": null}}, "5230ba1b0dd24240b06cca46b8007177": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_837fb72766734a48a3065c23661fc7d0", "IPY_MODEL_b7b4c7ba3a954c8b9e3e280b78e7e3d8", "IPY_MODEL_f30b153ace054a76a3c45bf65457b29d", "IPY_MODEL_a2d0f6c98d6347c6add188bd4d222088", "IPY_MODEL_64b0689dd6704e8ebfbbc3b29536cb85", "IPY_MODEL_a2d0f6c98d6347c6add188bd4d222088", "IPY_MODEL_4dbeb1d580714823ad85260339c0a44d", "IPY_MODEL_a2d0f6c98d6347c6add188bd4d222088", "IPY_MODEL_92a72e355c0f4b919bbb12019a97ae57"], "layout": "IPY_MODEL_cd82993c9d294c04ba846b45d66aef11"}}, "837fb72766734a48a3065c23661fc7d0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bcfdd97ca23341228df78da1bea652fc", "style": "IPY_MODEL_3075f11ed80349e2be74d7fe9f872dfb", "value": "<style type=\"text/css\">\n#T_d6b88_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d6b88_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d6b88_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d6b88_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d6b88_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d6b88_\">\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_d6b88_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row0_col1\" class=\"data row0 col1\" ><a href=\"#DatasetsSizeComparison_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\">Datasets Size Comparison</a></td>\n      <td id=\"T_d6b88_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_d6b88_row0_col3\" class=\"data row0 col3\" >Test-Train size ratio is 0.34</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row1_col1\" class=\"data row1 col1\" ><a href=\"#TrainTestSamplesMix_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\">Train Test Samples Mix</a></td>\n      <td id=\"T_d6b88_row1_col2\" class=\"data row1 col2\" >Percentage of test data samples that appear in train data is less or equal to 10%</td>\n      <td id=\"T_d6b88_row1_col3\" class=\"data row1 col3\" >Percent of test data samples that appear in train data: 2.63%</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row2_col1\" class=\"data row2 col1\" ><a href=\"#TrainTestFeatureDrift_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\">Train Test Feature Drift</a></td>\n      <td id=\"T_d6b88_row2_col2\" class=\"data row2 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_d6b88_row2_col3\" class=\"data row2 col3\" >Passed for 4 columns out of 4 columns.\nFound column \"sepal width (cm)\" has the highest numerical drift score: 0.07</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row3_col1\" class=\"data row3 col1\" ><a href=\"#TrainTestLabelDrift_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\">Train Test Label Drift</a></td>\n      <td id=\"T_d6b88_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_d6b88_row3_col3\" class=\"data row3 col3\" >Label's drift score Cramer's V is 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row4_col1\" class=\"data row4 col1\" >String Length Out Of Bounds - Test Dataset</td>\n      <td id=\"T_d6b88_row4_col2\" class=\"data row4 col2\" >Ratio of string length outliers is less or equal to 0%</td>\n      <td id=\"T_d6b88_row4_col3\" class=\"data row4 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row5_col1\" class=\"data row5 col1\" >String Length Out Of Bounds - Train Dataset</td>\n      <td id=\"T_d6b88_row5_col2\" class=\"data row5 col2\" >Ratio of string length outliers is less or equal to 0%</td>\n      <td id=\"T_d6b88_row5_col3\" class=\"data row5 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row6_col1\" class=\"data row6 col1\" >Data Duplicates - Test Dataset</td>\n      <td id=\"T_d6b88_row6_col2\" class=\"data row6 col2\" >Duplicate data ratio is less or equal to 0%</td>\n      <td id=\"T_d6b88_row6_col3\" class=\"data row6 col3\" >Found 0% duplicate data</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row7_col1\" class=\"data row7 col1\" >Data Duplicates - Train Dataset</td>\n      <td id=\"T_d6b88_row7_col2\" class=\"data row7 col2\" >Duplicate data ratio is less or equal to 0%</td>\n      <td id=\"T_d6b88_row7_col3\" class=\"data row7 col3\" >Found 0% duplicate data</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row8_col1\" class=\"data row8 col1\" >String Mismatch - Test Dataset</td>\n      <td id=\"T_d6b88_row8_col2\" class=\"data row8 col2\" >No string variants</td>\n      <td id=\"T_d6b88_row8_col3\" class=\"data row8 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row9_col1\" class=\"data row9 col1\" >String Mismatch - Train Dataset</td>\n      <td id=\"T_d6b88_row9_col2\" class=\"data row9 col2\" >No string variants</td>\n      <td id=\"T_d6b88_row9_col3\" class=\"data row9 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row10_col0\" class=\"data row10 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row10_col1\" class=\"data row10 col1\" >Mixed Data Types - Test Dataset</td>\n      <td id=\"T_d6b88_row10_col2\" class=\"data row10 col2\" >Rare data types in column are either more than 10% or less than 1% of the data</td>\n      <td id=\"T_d6b88_row10_col3\" class=\"data row10 col3\" >5 columns passed: found 0 columns with negligible types mix, and 5 columns without any types mix</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row11_col0\" class=\"data row11 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row11_col1\" class=\"data row11 col1\" >Mixed Data Types - Train Dataset</td>\n      <td id=\"T_d6b88_row11_col2\" class=\"data row11 col2\" >Rare data types in column are either more than 10% or less than 1% of the data</td>\n      <td id=\"T_d6b88_row11_col3\" class=\"data row11 col3\" >5 columns passed: found 0 columns with negligible types mix, and 5 columns without any types mix</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row12_col0\" class=\"data row12 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row12_col1\" class=\"data row12 col1\" >Mixed Nulls - Test Dataset</td>\n      <td id=\"T_d6b88_row12_col2\" class=\"data row12 col2\" >Number of different null types is less or equal to 1</td>\n      <td id=\"T_d6b88_row12_col3\" class=\"data row12 col3\" >Passed for 5 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row13_col0\" class=\"data row13 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row13_col1\" class=\"data row13 col1\" >Special Characters - Test Dataset</td>\n      <td id=\"T_d6b88_row13_col2\" class=\"data row13 col2\" >Ratio of samples containing solely special character is less or equal to 0.1%</td>\n      <td id=\"T_d6b88_row13_col3\" class=\"data row13 col3\" >Passed for 5 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row14_col0\" class=\"data row14 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row14_col1\" class=\"data row14 col1\" >Conflicting Labels - Train Dataset</td>\n      <td id=\"T_d6b88_row14_col2\" class=\"data row14 col2\" >Ambiguous sample ratio is less or equal to 0%</td>\n      <td id=\"T_d6b88_row14_col3\" class=\"data row14 col3\" >Ratio of samples with conflicting labels: 0%</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row15_col0\" class=\"data row15 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row15_col1\" class=\"data row15 col1\" >Special Characters - Train Dataset</td>\n      <td id=\"T_d6b88_row15_col2\" class=\"data row15 col2\" >Ratio of samples containing solely special character is less or equal to 0.1%</td>\n      <td id=\"T_d6b88_row15_col3\" class=\"data row15 col3\" >Passed for 5 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row16_col0\" class=\"data row16 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row16_col1\" class=\"data row16 col1\" >Single Value in Column - Test Dataset</td>\n      <td id=\"T_d6b88_row16_col2\" class=\"data row16 col2\" >Does not contain only a single value</td>\n      <td id=\"T_d6b88_row16_col3\" class=\"data row16 col3\" >Passed for 5 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row17_col0\" class=\"data row17 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row17_col1\" class=\"data row17 col1\" >Single Value in Column - Train Dataset</td>\n      <td id=\"T_d6b88_row17_col2\" class=\"data row17 col2\" >Does not contain only a single value</td>\n      <td id=\"T_d6b88_row17_col3\" class=\"data row17 col3\" >Passed for 5 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row18_col0\" class=\"data row18 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row18_col1\" class=\"data row18 col1\" >Whole Dataset Drift</td>\n      <td id=\"T_d6b88_row18_col2\" class=\"data row18 col2\" >Drift value is less than 0.25</td>\n      <td id=\"T_d6b88_row18_col3\" class=\"data row18 col3\" >Found drift value of: 0, corresponding to a domain classifier AUC of: 0.45</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row19_col0\" class=\"data row19 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row19_col1\" class=\"data row19 col1\" >String Mismatch Comparison</td>\n      <td id=\"T_d6b88_row19_col2\" class=\"data row19 col2\" >No new variants allowed in test data</td>\n      <td id=\"T_d6b88_row19_col3\" class=\"data row19 col3\" >No relevant columns to check were found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row20_col0\" class=\"data row20 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row20_col1\" class=\"data row20 col1\" >Category Mismatch Train Test</td>\n      <td id=\"T_d6b88_row20_col2\" class=\"data row20 col2\" >Ratio of samples with a new category is less or equal to 0%</td>\n      <td id=\"T_d6b88_row20_col3\" class=\"data row20 col3\" >Passed for 2 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row21_col0\" class=\"data row21 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row21_col1\" class=\"data row21 col1\" >New Label Train Test</td>\n      <td id=\"T_d6b88_row21_col2\" class=\"data row21 col2\" >Number of new label values is less or equal to 0</td>\n      <td id=\"T_d6b88_row21_col3\" class=\"data row21 col3\" >No new labels found</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row22_col0\" class=\"data row22 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row22_col1\" class=\"data row22 col1\" >Mixed Nulls - Train Dataset</td>\n      <td id=\"T_d6b88_row22_col2\" class=\"data row22 col2\" >Number of different null types is less or equal to 1</td>\n      <td id=\"T_d6b88_row22_col3\" class=\"data row22 col3\" >Passed for 5 relevant columns</td>\n    </tr>\n    <tr>\n      <td id=\"T_d6b88_row23_col0\" class=\"data row23 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d6b88_row23_col1\" class=\"data row23 col1\" >Conflicting Labels - Test Dataset</td>\n      <td id=\"T_d6b88_row23_col2\" class=\"data row23 col2\" >Ambiguous sample ratio is less or equal to 0%</td>\n      <td id=\"T_d6b88_row23_col3\" class=\"data row23 col3\" >Ratio of samples with conflicting labels: 0%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bcfdd97ca23341228df78da1bea652fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3075f11ed80349e2be74d7fe9f872dfb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7b4c7ba3a954c8b9e3e280b78e7e3d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_634fc5292ab740568a1c9c7fdb4ac81d", "style": "IPY_MODEL_d95677283e06415a917c57443b382b69", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "634fc5292ab740568a1c9c7fdb4ac81d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d95677283e06415a917c57443b382b69": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f30b153ace054a76a3c45bf65457b29d": {"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_f75f79f87744414f86c10c702e2d1b1c", "IPY_MODEL_d53c7344e61940f1b3565992bd3a6729", "IPY_MODEL_34645fee410d4dda945ad4b8a66d738e", "IPY_MODEL_f56828606f664eb192638a42da326e68"], "layout": "IPY_MODEL_62710e57ca13451ab8dde6652da70ca1"}}, "f75f79f87744414f86c10c702e2d1b1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bc76a4552432490eba709b9863fee2fa", "style": "IPY_MODEL_d8c38d4789c040319190bcce15419ccd", "value": "<h4 id=\"DatasetsSizeComparison_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\"><b>Datasets Size Comparison</b></h4>"}}, "bc76a4552432490eba709b9863fee2fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8c38d4789c040319190bcce15419ccd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d53c7344e61940f1b3565992bd3a6729": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ef4341087b04424db56cc417378ab76e", "style": "IPY_MODEL_24d059a1d1d14fc09a902625f78dd3b5", "value": "<p>Verify test dataset size comparing it to the train dataset size. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_datasets_size_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ef4341087b04424db56cc417378ab76e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "24d059a1d1d14fc09a902625f78dd3b5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "34645fee410d4dda945ad4b8a66d738e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9d50adef62154bbfb6550697479d9651", "style": "IPY_MODEL_760f784fa143409eb4c005f7b65aa740", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3191a_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3191a_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3191a_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3191a_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3191a_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3191a_\">\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_3191a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3191a_row0_col1\" class=\"data row0 col1\" >Test-Train size ratio is greater than 0.01</td>\n      <td id=\"T_3191a_row0_col2\" class=\"data row0 col2\" >Test-Train size ratio is 0.34</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9d50adef62154bbfb6550697479d9651": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "760f784fa143409eb4c005f7b65aa740": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f56828606f664eb192638a42da326e68": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6a86c2f36c014d4180c13d736e93e8ab", "IPY_MODEL_ee8ac07701b8461fa9e65cdd920be995", "IPY_MODEL_b3826fe4d70d4a78a877138997bd473f"], "layout": "IPY_MODEL_de9adfb74dfc438e9d37bb161386a54c"}}, "6a86c2f36c014d4180c13d736e93e8ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8e17af99a268421a81eb52db8e47daef", "style": "IPY_MODEL_995b8cd68d8643fbbe4a54d7ea386c3a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8e17af99a268421a81eb52db8e47daef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "995b8cd68d8643fbbe4a54d7ea386c3a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ee8ac07701b8461fa9e65cdd920be995": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_186d5400151c418da445222788666fce", "style": "IPY_MODEL_27a8c4ec20004bccba9d9c19d4840605", "value": "<style type=\"text/css\">\n#T_a9953_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a9953_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a9953_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a9953_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a9953_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a9953_\">\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_a9953_level0_row0\" class=\"row_heading level0 row0\" >Size</th>\n      <td id=\"T_a9953_row0_col0\" class=\"data row0 col0\" >112</td>\n      <td id=\"T_a9953_row0_col1\" class=\"data row0 col1\" >38</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "186d5400151c418da445222788666fce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "27a8c4ec20004bccba9d9c19d4840605": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b3826fe4d70d4a78a877138997bd473f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6c496b8bfcaf4d9cbc05df51bf9ff3de", "style": "IPY_MODEL_8dc7333cb5a246edb28965aac42d4829", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\" style=\"font-size: 14px;\">Go to top</a>"}}, "6c496b8bfcaf4d9cbc05df51bf9ff3de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8dc7333cb5a246edb28965aac42d4829": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "de9adfb74dfc438e9d37bb161386a54c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "62710e57ca13451ab8dde6652da70ca1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2d0f6c98d6347c6add188bd4d222088": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ca2282752f21467188460a89cb591d64", "style": "IPY_MODEL_3d8b91babb82475a88a832541f4e986c", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "ca2282752f21467188460a89cb591d64": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d8b91babb82475a88a832541f4e986c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "64b0689dd6704e8ebfbbc3b29536cb85": {"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_82b9dc8221584af28fade05f96765fdc", "IPY_MODEL_6a08a62162fe4a2ab14c7131029627e5", "IPY_MODEL_6bac674e43004712a16f11460604a3eb", "IPY_MODEL_decb11bfbb5c47a086e0ecf4a24c5721"], "layout": "IPY_MODEL_a4f1ee80c97f4446b67b5510c1a3c086"}}, "82b9dc8221584af28fade05f96765fdc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_316671b0ab404695837b754266dc8378", "style": "IPY_MODEL_73d521d604364e1f9c84290213920cdf", "value": "<h4 id=\"TrainTestSamplesMix_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\"><b>Train Test Samples Mix</b></h4>"}}, "316671b0ab404695837b754266dc8378": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73d521d604364e1f9c84290213920cdf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6a08a62162fe4a2ab14c7131029627e5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9594df05f76847999b8f2fbd784cc28c", "style": "IPY_MODEL_9cd1b3a3d6a943ed883a49539a5e7b4b", "value": "<p>Detect samples in the test data that appear also in training data. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_samples_mix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9594df05f76847999b8f2fbd784cc28c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9cd1b3a3d6a943ed883a49539a5e7b4b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6bac674e43004712a16f11460604a3eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ef45a88ee5e64dc78812efa927bfe550", "style": "IPY_MODEL_d320a57943ff4c83bebbd47fc1f3130c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8da13_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8da13_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8da13_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8da13_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8da13_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8da13_\">\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_8da13_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8da13_row0_col1\" class=\"data row0 col1\" >Percentage of test data samples that appear in train data is less or equal to 10%</td>\n      <td id=\"T_8da13_row0_col2\" class=\"data row0 col2\" >Percent of test data samples that appear in train data: 2.63%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ef45a88ee5e64dc78812efa927bfe550": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d320a57943ff4c83bebbd47fc1f3130c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "decb11bfbb5c47a086e0ecf4a24c5721": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a1045c59d1ec4bc69345be0af781ed3a", "IPY_MODEL_5a373b842f754264b64bf6f74701d8cc", "IPY_MODEL_8ff01227877d402a98b9a85c89faeab8", "IPY_MODEL_2e0c2bad621548adbd7326817cf09a95"], "layout": "IPY_MODEL_454cf417991742be9cc96e4ca39f9afd"}}, "a1045c59d1ec4bc69345be0af781ed3a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3b32877fd51e4b5d8740529e46b21e45", "style": "IPY_MODEL_5d50f3dbb08149eeba8eb1e98aa6c53e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3b32877fd51e4b5d8740529e46b21e45": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d50f3dbb08149eeba8eb1e98aa6c53e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5a373b842f754264b64bf6f74701d8cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_84feecf2b2ec4a2faf43af3efe3517d8", "style": "IPY_MODEL_2895f0e954aa4170b1899a780f685411", "value": "<div>2.63% (1 / 38)                      of test data samples appear in train data</div>"}}, "84feecf2b2ec4a2faf43af3efe3517d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2895f0e954aa4170b1899a780f685411": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8ff01227877d402a98b9a85c89faeab8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6746bdf8d5214a62ba9ffc6149e20db9", "style": "IPY_MODEL_eb05e9a0975048f98eacd0e01541bfb1", "value": "<style type=\"text/css\">\n#T_67aa7_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67aa7_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67aa7_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67aa7_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_67aa7_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_67aa7_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >sepal length (cm)</th>\n      <th class=\"col_heading level0 col1\" >sepal width (cm)</th>\n      <th class=\"col_heading level0 col2\" >petal length (cm)</th>\n      <th class=\"col_heading level0 col3\" >petal width (cm)</th>\n      <th class=\"col_heading level0 col4\" >target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_67aa7_level0_row0\" class=\"row_heading level0 row0\" >Train indices: 30\nTest indices: 28</th>\n      <td id=\"T_67aa7_row0_col0\" class=\"data row0 col0\" >5.80</td>\n      <td id=\"T_67aa7_row0_col1\" class=\"data row0 col1\" >2.70</td>\n      <td id=\"T_67aa7_row0_col2\" class=\"data row0 col2\" >5.10</td>\n      <td id=\"T_67aa7_row0_col3\" class=\"data row0 col3\" >1.90</td>\n      <td id=\"T_67aa7_row0_col4\" class=\"data row0 col4\" >2.00</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6746bdf8d5214a62ba9ffc6149e20db9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eb05e9a0975048f98eacd0e01541bfb1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2e0c2bad621548adbd7326817cf09a95": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f04ef07fd24433d8e28234bdffe10d2", "style": "IPY_MODEL_85c1491f7bee4697a1c0272effe97bb2", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\" style=\"font-size: 14px;\">Go to top</a>"}}, "3f04ef07fd24433d8e28234bdffe10d2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "85c1491f7bee4697a1c0272effe97bb2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "454cf417991742be9cc96e4ca39f9afd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a4f1ee80c97f4446b67b5510c1a3c086": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4dbeb1d580714823ad85260339c0a44d": {"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_f096898ab75f432ba719a47b5652efba", "IPY_MODEL_b24951e845ea43b79a8bf8507a51ce09", "IPY_MODEL_a8574b18d8684913a0996420f40344ea", "IPY_MODEL_68e60b61bfbd4a8aa9e01210e4c2a059"], "layout": "IPY_MODEL_2a2740f6963b4a218d2f1e0eb47e1114"}}, "f096898ab75f432ba719a47b5652efba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b20720ded4c54f39af24cf25df1ce1f2", "style": "IPY_MODEL_4a827af55ae04c82b8229308827a04d7", "value": "<h4 id=\"TrainTestFeatureDrift_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\"><b>Train Test Feature Drift</b></h4>"}}, "b20720ded4c54f39af24cf25df1ce1f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4a827af55ae04c82b8229308827a04d7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b24951e845ea43b79a8bf8507a51ce09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e956f1f8b4534925b5939ff39ec6941f", "style": "IPY_MODEL_b8365c625a5a49ee90cf8562205dee03", "value": "<p>    Calculate drift between train dataset and test dataset per feature, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_feature_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e956f1f8b4534925b5939ff39ec6941f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b8365c625a5a49ee90cf8562205dee03": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a8574b18d8684913a0996420f40344ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b5c58d7c0c040ea83f5d80d8fe21d03", "style": "IPY_MODEL_554a762c94b341b28bcab2d40c37c029", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6f5c1_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f5c1_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f5c1_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f5c1_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6f5c1_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6f5c1_\">\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_6f5c1_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6f5c1_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1</td>\n      <td id=\"T_6f5c1_row0_col2\" class=\"data row0 col2\" >Passed for 4 columns out of 4 columns.\nFound column \"sepal width (cm)\" has the highest numerical drift score: 0.07</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1b5c58d7c0c040ea83f5d80d8fe21d03": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "554a762c94b341b28bcab2d40c37c029": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "68e60b61bfbd4a8aa9e01210e4c2a059": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9fa6e02dd8ef42d686d798901adc1a20", "IPY_MODEL_8dbb7ea5008f45e18d417293ca9e8979", "IPY_MODEL_fced1c6501f1414c9ca305324d3fe595", "IPY_MODEL_d80f7a4871de435a979e9240cb3781f6", "IPY_MODEL_e82ac36acfac49328a61a430b16cddb4", "IPY_MODEL_ef849d0d7b5843e696e61fddaef62aa3", "IPY_MODEL_9d33e61380004faab6605b6fdc9cf3ed"], "layout": "IPY_MODEL_50e40d700587498eb270fcae52b7cc63"}}, "9fa6e02dd8ef42d686d798901adc1a20": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8c05f9b65ea145fbb09b502a1e7880bc", "style": "IPY_MODEL_0eb5b4a2118c49e0af206286cbcf7878", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8c05f9b65ea145fbb09b502a1e7880bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0eb5b4a2118c49e0af206286cbcf7878": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8dbb7ea5008f45e18d417293ca9e8979": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_969099897d2d45669a7da7330a63400f", "style": "IPY_MODEL_f02de4b8a1dc4d80844a8dcde09bf680", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the features, sorted\n                by drift score and showing only the top 5 features, according to drift score.\n                <br>If available, the plot titles also show the feature importance (FI) rank.\n            </span></div>"}}, "969099897d2d45669a7da7330a63400f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f02de4b8a1dc4d80844a8dcde09bf680": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fced1c6501f1414c9ca305324d3fe595": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.06863808252697143], 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"model_module_version": "1.2.0", "state": {}}, "92a72e355c0f4b919bbb12019a97ae57": {"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_d84c3d524b2e435680da0ba461992da5", "IPY_MODEL_bd515c137aad4d0abacf521aec59a3a2", "IPY_MODEL_a2b94df6040e4e41bdfabf3fa4c30424", "IPY_MODEL_19b9978b03e44034b024e1c94b5d8593"], "layout": "IPY_MODEL_2fe5435ab9f7499f9a5e40f87c01a4e0"}}, "d84c3d524b2e435680da0ba461992da5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f71b79c0648346ddb698b2b61cd43770", "style": "IPY_MODEL_82c67e1de60a43ffa01afd153a228a70", "value": "<h4 id=\"TrainTestLabelDrift_2XRW6TM2O04K0LTHY25M8KB9W-section-1ZLBO\"><b>Train Test Label Drift</b></h4>"}}, "f71b79c0648346ddb698b2b61cd43770": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82c67e1de60a43ffa01afd153a228a70": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bd515c137aad4d0abacf521aec59a3a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bffcd9699e394c4e81fdf4d3dbedaa88", "style": "IPY_MODEL_58d7cbab2e184b3d9430351c1d150e63", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/train_test_validation/plot_train_test_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "bffcd9699e394c4e81fdf4d3dbedaa88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58d7cbab2e184b3d9430351c1d150e63": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a2b94df6040e4e41bdfabf3fa4c30424": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3be2f66a4fb549209518cbc0b6bfa655", "style": "IPY_MODEL_2b04e0071b6f4292b41878b324b2a9cd", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_55b61_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_55b61_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_55b61_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_55b61_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_55b61_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_55b61_\">\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_55b61_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_55b61_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.2 and numerical drift score < 0.1 for label drift</td>\n      <td id=\"T_55b61_row0_col2\" class=\"data row0 col2\" >Label's drift score Cramer's V is 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3be2f66a4fb549209518cbc0b6bfa655": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b04e0071b6f4292b41878b324b2a9cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "19b9978b03e44034b024e1c94b5d8593": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1e0c047b8a6443178e5520441f2dd011", "IPY_MODEL_3ef5e7e4a617422f98af291f421711f6", "IPY_MODEL_12db96f660ae4aaabb5dcf03e4c6c4c9", "IPY_MODEL_67ec384c27c248c6b1bc06a69b069d47"], "layout": "IPY_MODEL_fbaf685c31e8441fa88d4a2f6ffbadc0"}}, "1e0c047b8a6443178e5520441f2dd011": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10ffa99b85e0405eb23e834f1b8b7254", "style": "IPY_MODEL_777e46b380c74b2f956908183068fc7e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "10ffa99b85e0405eb23e834f1b8b7254": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "777e46b380c74b2f956908183068fc7e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3ef5e7e4a617422f98af291f421711f6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c73834171c9b4abb8856127926e44d89", "style": "IPY_MODEL_61a9feda91d2446b8e560fc3296d7e79", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the label.\n            </span></div>"}}, "c73834171c9b4abb8856127926e44d89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61a9feda91d2446b8e560fc3296d7e79": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12db96f660ae4aaabb5dcf03e4c6c4c9": {"model_name": "FigureModel", 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"jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_815dab96bf0d4c6fb8af9ce1a44714d1"], "layout": "IPY_MODEL_7f7de7469092476fbf3875306e3db5e1", "selected_index": null}}, "815dab96bf0d4c6fb8af9ce1a44714d1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_af645904350845e28e4ad510fdca8911", "IPY_MODEL_82d11cc133e340f7bc5fe411a6181fbf", "IPY_MODEL_906b40384ba94504980509ffe047887c"], "layout": "IPY_MODEL_7d100efea87c4117b8e403db9e3d575c"}}, "af645904350845e28e4ad510fdca8911": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6bfb6af8b73d4b3bb076e0ca831b12e4", "style": "IPY_MODEL_1069f000165a42ffb024825a1f105353", "value": "<style type=\"text/css\">\n#T_bc984_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc984_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc984_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc984_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_bc984_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_bc984_\">\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_bc984_row0_col0\" class=\"data row0 col0\" ><a href=\"#OutlierSampleDetection-TrainDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-RIQ8H\">Outlier Sample Detection - Train Dataset</a></td>\n      <td id=\"T_bc984_row0_col1\" class=\"data row0 col1\" >Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/data_integrity/plot_outlier_sample_detection.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6bfb6af8b73d4b3bb076e0ca831b12e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1069f000165a42ffb024825a1f105353": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "82d11cc133e340f7bc5fe411a6181fbf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cda708ad56c24cc3aaef0105149c2193", "style": "IPY_MODEL_16d80cdadde446f28e7c3fc0d0a13f87", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "cda708ad56c24cc3aaef0105149c2193": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16d80cdadde446f28e7c3fc0d0a13f87": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "906b40384ba94504980509ffe047887c": {"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_256a2c9b7db542d1bcc2faebb027b692", "IPY_MODEL_43cc374acd5f4ad99ec2ccb2c735f92d", "IPY_MODEL_34f94df986514503b06db6d057fb2b82"], "layout": "IPY_MODEL_c988758a068a4e69b6a9b0e1f4c9d41b"}}, "256a2c9b7db542d1bcc2faebb027b692": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51e9d306d70d42d6b8f08081c46e477e", "style": "IPY_MODEL_4fb9c73e74244898a294767f73e5e560", "value": "<h4 id=\"OutlierSampleDetection-TrainDataset_2XRW6TM2O04K0LTHY25M8KB9W-section-RIQ8H\"><b>Outlier Sample Detection - Train Dataset</b></h4>"}}, "51e9d306d70d42d6b8f08081c46e477e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4fb9c73e74244898a294767f73e5e560": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "43cc374acd5f4ad99ec2ccb2c735f92d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_de1f755011c24ac58a1f9f0d2de8f0ff", "style": "IPY_MODEL_b588547ec3664e39a7462d1cb0bb6de0", "value": "<p>Detects outliers in a dataset using the LoOP algorithm. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/data_integrity/plot_outlier_sample_detection.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "de1f755011c24ac58a1f9f0d2de8f0ff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b588547ec3664e39a7462d1cb0bb6de0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "34f94df986514503b06db6d057fb2b82": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d68b58471d2a4cdba7c937e7d6416798", "IPY_MODEL_a84fe66be2ab4bc0b2fc774318ea66cc", "IPY_MODEL_863b326b87314b6cb58b64ae03449a83", "IPY_MODEL_581e3a8840744d62808a1432ab65e17e"], "layout": "IPY_MODEL_50f9df76f30243d1adbea394348d52f2"}}, "d68b58471d2a4cdba7c937e7d6416798": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a46e17adb4584325b3fdf517aa127ea1", "style": "IPY_MODEL_9068b2128dd34430b4854b0559860b2a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a46e17adb4584325b3fdf517aa127ea1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9068b2128dd34430b4854b0559860b2a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a84fe66be2ab4bc0b2fc774318ea66cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10f5c727b5554a11914a77d41082a81f", "style": "IPY_MODEL_5a9adfc607254ccabebb97ecc05fe79c", "value": "<div><span>\n                    The Outlier Probability Score is calculated by the LoOP algorithm which measures the local deviation\n                    of density of a given sample with respect to its neighbors. These outlier scores are directly\n                    interpretable as a probability of an object being an outlier (see\n                    <a href=\"https://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf\"\n                    target=\"_blank\" rel=\"noopener noreferrer\">link</a> for more information).<br><br>\n                    </span></div>"}}, "10f5c727b5554a11914a77d41082a81f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5a9adfc607254ccabebb97ecc05fe79c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "863b326b87314b6cb58b64ae03449a83": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_59e819ad61104211b8d271d42edc4618", "style": "IPY_MODEL_7d34c267ebcc42a1a00874df12786e89", "value": "<style type=\"text/css\">\n#T_dac91_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dac91_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dac91_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dac91_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dac91_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_dac91_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >Outlier Probability Score</th>\n      <th class=\"col_heading level0 col1\" >sepal length (cm)</th>\n      <th class=\"col_heading level0 col2\" >sepal width (cm)</th>\n      <th class=\"col_heading level0 col3\" >petal length (cm)</th>\n      <th class=\"col_heading level0 col4\" >petal width (cm)</th>\n      <th class=\"col_heading level0 col5\" >target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_dac91_level0_row0\" class=\"row_heading level0 row0\" >56</th>\n      <td id=\"T_dac91_row0_col0\" class=\"data row0 col0\" >0.89</td>\n      <td id=\"T_dac91_row0_col1\" class=\"data row0 col1\" >4.50</td>\n      <td id=\"T_dac91_row0_col2\" class=\"data row0 col2\" >2.30</td>\n      <td id=\"T_dac91_row0_col3\" class=\"data row0 col3\" >1.30</td>\n      <td id=\"T_dac91_row0_col4\" class=\"data row0 col4\" >0.30</td>\n      <td id=\"T_dac91_row0_col5\" class=\"data row0 col5\" >0</td>\n    </tr>\n    <tr>\n      <th id=\"T_dac91_level0_row1\" class=\"row_heading level0 row1\" >4</th>\n      <td id=\"T_dac91_row1_col0\" class=\"data row1 col0\" >0.72</td>\n      <td id=\"T_dac91_row1_col1\" class=\"data row1 col1\" >4.90</td>\n      <td id=\"T_dac91_row1_col2\" class=\"data row1 col2\" >2.50</td>\n      <td id=\"T_dac91_row1_col3\" class=\"data row1 col3\" >4.50</td>\n      <td id=\"T_dac91_row1_col4\" class=\"data row1 col4\" >1.70</td>\n      <td id=\"T_dac91_row1_col5\" class=\"data row1 col5\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_dac91_level0_row2\" class=\"row_heading level0 row2\" >82</th>\n      <td id=\"T_dac91_row2_col0\" class=\"data row2 col0\" >0.57</td>\n      <td id=\"T_dac91_row2_col1\" class=\"data row2 col1\" >6.30</td>\n      <td id=\"T_dac91_row2_col2\" class=\"data row2 col2\" >3.30</td>\n      <td id=\"T_dac91_row2_col3\" class=\"data row2 col3\" >4.70</td>\n      <td id=\"T_dac91_row2_col4\" class=\"data row2 col4\" >1.60</td>\n      <td id=\"T_dac91_row2_col5\" class=\"data row2 col5\" >1</td>\n    </tr>\n    <tr>\n      <th id=\"T_dac91_level0_row3\" class=\"row_heading level0 row3\" >13</th>\n      <td id=\"T_dac91_row3_col0\" class=\"data row3 col0\" >0.56</td>\n      <td id=\"T_dac91_row3_col1\" class=\"data row3 col1\" >5.80</td>\n      <td id=\"T_dac91_row3_col2\" class=\"data row3 col2\" >2.80</td>\n      <td id=\"T_dac91_row3_col3\" class=\"data row3 col3\" >5.10</td>\n      <td id=\"T_dac91_row3_col4\" class=\"data row3 col4\" >2.40</td>\n      <td id=\"T_dac91_row3_col5\" class=\"data row3 col5\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_dac91_level0_row4\" class=\"row_heading level0 row4\" >93</th>\n      <td id=\"T_dac91_row4_col0\" class=\"data row4 col0\" >0.56</td>\n      <td id=\"T_dac91_row4_col1\" class=\"data row4 col1\" >4.60</td>\n      <td id=\"T_dac91_row4_col2\" class=\"data row4 col2\" >3.60</td>\n      <td id=\"T_dac91_row4_col3\" class=\"data row4 col3\" >1.00</td>\n      <td id=\"T_dac91_row4_col4\" class=\"data row4 col4\" >0.20</td>\n      <td id=\"T_dac91_row4_col5\" class=\"data row4 col5\" >0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "59e819ad61104211b8d271d42edc4618": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d34c267ebcc42a1a00874df12786e89": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "581e3a8840744d62808a1432ab65e17e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cf7021ef88374819b3268df7532226a8", "style": "IPY_MODEL_8c982aebcd664015a44ebcf1589fe29c", "value": "<br><a href=\"#summary_2XRW6TM2O04K0LTHY25M8KB9W-section-RIQ8H\" style=\"font-size: 14px;\">Go to top</a>"}}, "cf7021ef88374819b3268df7532226a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8c982aebcd664015a44ebcf1589fe29c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "50f9df76f30243d1adbea394348d52f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c988758a068a4e69b6a9b0e1f4c9d41b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d100efea87c4117b8e403db9e3d575c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7f7de7469092476fbf3875306e3db5e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c0e0f14ef1ba424ab8367e726385f831": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33c4fdd966644f9f9291a6e562e5a72f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5808701aba0e46d4b4ac785f70ec9d94", "IPY_MODEL_253df8c11eaf4bdc8d095dc175870e75"], "layout": "IPY_MODEL_4779ee702dc24a93b4a2501108db3f3c"}}, "5808701aba0e46d4b4ac785f70ec9d94": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d2ba87969e8c4cd29228d88eaf421047", "style": "IPY_MODEL_bb5356a1be89488ca25a9d241e4feee9"}}, "d2ba87969e8c4cd29228d88eaf421047": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bb5356a1be89488ca25a9d241e4feee9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "253df8c11eaf4bdc8d095dc175870e75": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Run"}, "children": ["IPY_MODEL_0c2adb63e9664a4b87fe7fc1fa0943a9"], "layout": "IPY_MODEL_ed60d5527f2f416393b613abdbb493df", "selected_index": null}}, "0c2adb63e9664a4b87fe7fc1fa0943a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f92bf0c04ab243779663985d0356e7c7", "style": "IPY_MODEL_4f52dd180dd04dfbab5c703e0f62017d", "value": "<style type=\"text/css\">\n#T_e080d_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e080d_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e080d_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e080d_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e080d_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e080d_\">\n  <thead>\n    <tr>\n      <th class=\"col_heading level0 col0\" >Check</th>\n      <th class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e080d_row0_col0\" class=\"data row0 col0\" >Train Test Performance</td>\n      <td id=\"T_e080d_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row1_col0\" class=\"data row1 col0\" >Outlier Sample Detection - Test Dataset</td>\n      <td id=\"T_e080d_row1_col1\" class=\"data row1 col1\" >There are not enough samples to run this check, found only 38 samples.</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row2_col0\" class=\"data row2 col0\" >Index Train Test Leakage</td>\n      <td id=\"T_e080d_row2_col1\" class=\"data row2 col1\" >DatasetValidationError: Dataset does not contain an index. see <a href=\"https://docs.deepchecks.com/0.8/user-guide/tabular/dataset_object.html?html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Dataset docs</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row3_col0\" class=\"data row3 col0\" >Date Train Test Leakage Overlap</td>\n      <td id=\"T_e080d_row3_col1\" class=\"data row3 col1\" >DatasetValidationError: Dataset does not contain a datetime. see <a href=\"https://docs.deepchecks.com/0.8/user-guide/tabular/dataset_object.html?html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Dataset docs</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row4_col0\" class=\"data row4 col0\" >Date Train Test Leakage Duplicates</td>\n      <td id=\"T_e080d_row4_col1\" class=\"data row4 col1\" >DatasetValidationError: Dataset does not contain a datetime. see <a href=\"https://docs.deepchecks.com/0.8/user-guide/tabular/dataset_object.html?html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Dataset docs</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row5_col0\" class=\"data row5 col0\" >Model Inference Time - Test Dataset</td>\n      <td id=\"T_e080d_row5_col1\" class=\"data row5 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row6_col0\" class=\"data row6 col0\" >Model Inference Time - Train Dataset</td>\n      <td id=\"T_e080d_row6_col1\" class=\"data row6 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row7_col0\" class=\"data row7 col0\" >Boosting Overfit</td>\n      <td id=\"T_e080d_row7_col1\" class=\"data row7 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row8_col0\" class=\"data row8 col0\" >Unused Features</td>\n      <td id=\"T_e080d_row8_col1\" class=\"data row8 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row9_col0\" class=\"data row9 col0\" >Regression Error Distribution - Test Dataset</td>\n      <td id=\"T_e080d_row9_col1\" class=\"data row9 col1\" >ModelValidationError: Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row10_col0\" class=\"data row10 col0\" >Regression Error Distribution - Train Dataset</td>\n      <td id=\"T_e080d_row10_col1\" class=\"data row10 col1\" >ModelValidationError: Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row11_col0\" class=\"data row11 col0\" >Identifier Label Correlation - Train Dataset</td>\n      <td id=\"T_e080d_row11_col1\" class=\"data row11 col1\" >DatasetValidationError: Dataset does not contain an index or a datetime. see <a href=\"https://docs.deepchecks.com/0.8/user-guide/tabular/dataset_object.html?html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Dataset docs</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row12_col0\" class=\"data row12 col0\" >Regression Systematic Error - Test Dataset</td>\n      <td id=\"T_e080d_row12_col1\" class=\"data row12 col1\" >ModelValidationError: Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row13_col0\" class=\"data row13 col0\" >Calibration Score - Test Dataset</td>\n      <td id=\"T_e080d_row13_col1\" class=\"data row13 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row14_col0\" class=\"data row14 col0\" >Calibration Score - Train Dataset</td>\n      <td id=\"T_e080d_row14_col1\" class=\"data row14 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row15_col0\" class=\"data row15 col0\" >Weak Segments Performance - Test Dataset</td>\n      <td id=\"T_e080d_row15_col1\" class=\"data row15 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row16_col0\" class=\"data row16 col0\" >Weak Segments Performance - Train Dataset</td>\n      <td id=\"T_e080d_row16_col1\" class=\"data row16 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row17_col0\" class=\"data row17 col0\" >Simple Model Comparison</td>\n      <td id=\"T_e080d_row17_col1\" class=\"data row17 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row18_col0\" class=\"data row18 col0\" >Train Test Prediction Drift</td>\n      <td id=\"T_e080d_row18_col1\" class=\"data row18 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row19_col0\" class=\"data row19 col0\" >Confusion Matrix Report - Test Dataset</td>\n      <td id=\"T_e080d_row19_col1\" class=\"data row19 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row20_col0\" class=\"data row20 col0\" >Confusion Matrix Report - Train Dataset</td>\n      <td id=\"T_e080d_row20_col1\" class=\"data row20 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row21_col0\" class=\"data row21 col0\" >Roc Report - Test Dataset</td>\n      <td id=\"T_e080d_row21_col1\" class=\"data row21 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row22_col0\" class=\"data row22 col0\" >Roc Report - Train Dataset</td>\n      <td id=\"T_e080d_row22_col1\" class=\"data row22 col1\" >Check is irrelevant for Datasets without model</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row23_col0\" class=\"data row23 col0\" >Regression Systematic Error - Train Dataset</td>\n      <td id=\"T_e080d_row23_col1\" class=\"data row23 col1\" >ModelValidationError: Check is irrelevant for classification tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_e080d_row24_col0\" class=\"data row24 col0\" >Identifier Label Correlation - Test Dataset</td>\n      <td id=\"T_e080d_row24_col1\" class=\"data row24 col1\" >DatasetValidationError: Dataset does not contain an index or a datetime. see <a href=\"https://docs.deepchecks.com/0.8/user-guide/tabular/dataset_object.html?html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Dataset docs</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f92bf0c04ab243779663985d0356e7c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f52dd180dd04dfbab5c703e0f62017d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed60d5527f2f416393b613abdbb493df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4779ee702dc24a93b4a2501108db3f3c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45689cd67cd64c0fa33d4a0965a5a39c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ae69ec14fe34fb5a89503e8d9d6d290": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": 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.. _sphx_glr_download_user-guide_general_exporting_results_examples_plot_export_suite_results_as_html.py:

.. only:: html

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


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

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


.. only:: html

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

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