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

.. only:: html

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

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

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

.. _sphx_glr_user-guide_tabular_auto_quickstarts_plot_quick_model_evaluation.py:


.. _quick_model_evaluation:

Quickstart - Model Evaluation Suite
***********************************

The deepchecks model evaluation suite is relevant any time you wish to
evaluate your model. For example:

- Thorough analysis of the model's performance before deploying it.
- Evaluation of a proposed model during the model selection and optimization stage.
- Checking the model's performance on a new batch of data (with or without comparison to previous data batches).

Here we'll build a regression model using the wine quality dataset
(:mod:`deepchecks.tabular.datasets.regression.wine_quality`),
to demonstrate how you can run the suite with only a few simple lines of code, 
and see which kind of insights it can find.

.. code-block:: bash

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

    # or install using pip from your python environment

.. GENERATED FROM PYTHON SOURCE LINES 30-35

Prepare Data and Model
======================

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

.. GENERATED FROM PYTHON SOURCE LINES 35-41

.. code-block:: default


    from deepchecks.tabular.datasets.regression import wine_quality

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






.. raw:: html

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        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>fixed acidity</th>
          <th>volatile acidity</th>
          <th>citric acid</th>
          <th>residual sugar</th>
          <th>chlorides</th>
          <th>free sulfur dioxide</th>
          <th>total sulfur dioxide</th>
          <th>density</th>
          <th>pH</th>
          <th>sulphates</th>
          <th>alcohol</th>
          <th>quality</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>7.4</td>
          <td>0.70</td>
          <td>0.0</td>
          <td>1.9</td>
          <td>0.076</td>
          <td>11.0</td>
          <td>34.0</td>
          <td>0.9978</td>
          <td>3.51</td>
          <td>0.56</td>
          <td>9.4</td>
          <td>5</td>
        </tr>
        <tr>
          <th>1</th>
          <td>7.8</td>
          <td>0.88</td>
          <td>0.0</td>
          <td>2.6</td>
          <td>0.098</td>
          <td>25.0</td>
          <td>67.0</td>
          <td>0.9968</td>
          <td>3.20</td>
          <td>0.68</td>
          <td>9.8</td>
          <td>5</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 42-45

Split Data and Train a Simple Model
-----------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 45-52

.. code-block:: default

    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import GradientBoostingRegressor

    X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data['quality'], test_size=0.2, random_state=42)
    gbr = GradientBoostingRegressor()
    gbr.fit(X_train, y_train)





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

 .. code-block:: none


    GradientBoostingRegressor()



.. GENERATED FROM PYTHON SOURCE LINES 53-62

Run Deepchecks for Model Evaluation
===========================================

Create a Dataset Object
-------------------------

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

.. GENERATED FROM PYTHON SOURCE LINES 62-73

.. code-block:: default


    from deepchecks.tabular import Dataset

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

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

    train_ds = Dataset(X_train, label=y_train, cat_features=[])
    test_ds = Dataset(X_test, label=y_test, cat_features=[])








.. GENERATED FROM PYTHON SOURCE LINES 74-84

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

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

Check out the :doc:`"when should you use deepchecks guide" </getting-started/when_should_you_use>`
for some more info about the existing suites and when to use them.

.. GENERATED FROM PYTHON SOURCE LINES 84-93

.. code-block:: default


    from deepchecks.tabular.suites import model_evaluation

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





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

 .. code-block:: none


    Model Evaluation Suite:
    |            | 0/12 [Time: 00:00]
    Model Evaluation Suite:
    |#           | 1/12 [Time: 00:00, Check=Train Test Performance]
    Model Evaluation Suite:
    |#####       | 5/12 [Time: 00:00, Check=Simple Model Comparison]
    Model Evaluation Suite:
    |########    | 8/12 [Time: 00:03, Check=Regression Systematic Error]
    Model Evaluation Suite:
    |########### | 11/12 [Time: 00:04, Check=Boosting Overfit]          
                                                                                  

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        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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white-space: pre-wrap;\n}\n#T_8f4bb_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f4bb_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f4bb_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8f4bb_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8f4bb_\">\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_8f4bb_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_8f4bb_row0_col1\" class=\"data row0 col1\" ><a href=\"#RegressionSystematicError-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Regression Systematic Error - Test Dataset</a></td>\n      <td id=\"T_8f4bb_row0_col2\" class=\"data row0 col2\" >Bias ratio is less than 0.01</td>\n      <td id=\"T_8f4bb_row0_col3\" class=\"data row0 col3\" >Found bias ratio 0.05</td>\n    </tr>\n    <tr>\n      <td id=\"T_8f4bb_row1_col0\" class=\"data row1 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_8f4bb_row1_col1\" class=\"data row1 col1\" ><a href=\"#WeakSegmentsPerformance-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Weak Segments Performance - Train Dataset</a></td>\n      <td id=\"T_8f4bb_row1_col2\" class=\"data row1 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_8f4bb_row1_col3\" class=\"data row1 col3\" >Found a segment with Neg RMSE score of -0.675 in comparison to an average score of -0.499 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_8f4bb_row2_col0\" class=\"data row2 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_8f4bb_row2_col1\" class=\"data row2 col1\" ><a href=\"#WeakSegmentsPerformance-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Weak Segments Performance - Test Dataset</a></td>\n      <td id=\"T_8f4bb_row2_col2\" class=\"data row2 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_8f4bb_row2_col3\" class=\"data row2 col3\" >Found a segment with Neg RMSE score of -1.074 in comparison to an average score of -0.602 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "20e5bcb780e942b790f2a7dfe3ce4ec9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1541c484a1a4b4dbf3b13b5da5d58b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0a1d6e3907ad4dfe9e1ce1d1b844e7c2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e0005457e9e248b8b57ea3e5abaeb43e", "style": "IPY_MODEL_f5ef4ac85b2144af813a8152575b7ff2", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "e0005457e9e248b8b57ea3e5abaeb43e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f5ef4ac85b2144af813a8152575b7ff2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e7db51f3618f4e3a9d1ae07094ebd00b": {"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_e21c7f70a6fa4e8ea102599fbed55e55", "IPY_MODEL_8bd438736baf4ac1a1eeeb1b898ee729", "IPY_MODEL_85df6a8985844eb38a12b0d25e65d2ea", "IPY_MODEL_3c86a03ffb1846b88fc55ac64ab3c32b"], "layout": "IPY_MODEL_acb2ac3c5a5348c995bda4f5d0b1db7b"}}, "e21c7f70a6fa4e8ea102599fbed55e55": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d11f4bd97c0745a59c9372dd05ed1197", "style": "IPY_MODEL_c6f90a11b8454f32b219bc683c208b15", "value": "<h4 id=\"WeakSegmentsPerformance-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Weak Segments Performance - Train Dataset</b></h4>"}}, "d11f4bd97c0745a59c9372dd05ed1197": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6f90a11b8454f32b219bc683c208b15": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8bd438736baf4ac1a1eeeb1b898ee729": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_618b87900330449094fc6f64ef0195f0", "style": "IPY_MODEL_7d68677137bd4b559f1034c2ca752349", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "618b87900330449094fc6f64ef0195f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d68677137bd4b559f1034c2ca752349": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85df6a8985844eb38a12b0d25e65d2ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dda309fed5554c2682acf44cd51bce5b", "style": "IPY_MODEL_b05d6d39c9e54e7296b9cf23627610fa", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_80da2_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80da2_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80da2_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80da2_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80da2_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_80da2_\">\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_80da2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_80da2_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_80da2_row0_col2\" class=\"data row0 col2\" >Found a segment with Neg RMSE score of -0.675 in comparison to an average score of -0.499 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "dda309fed5554c2682acf44cd51bce5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b05d6d39c9e54e7296b9cf23627610fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c86a03ffb1846b88fc55ac64ab3c32b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a04f19e22eab42c382680d3bcdbf12f0", "IPY_MODEL_01065c8ee9664254b422620dc5e4d30c", "IPY_MODEL_4d9b23c7927042939f31b9e6401cdf60", "IPY_MODEL_58ee58f1698a4766bce92f7f7021c39a"], "layout": "IPY_MODEL_e4316af26c214661a6510d055ccf60b8"}}, "a04f19e22eab42c382680d3bcdbf12f0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_534e7dc61af1485eb66ab7d811b9c904", "style": "IPY_MODEL_5efa79b59b0b40a18ca6af43e2316439", "value": "<h5><b>Additional Outputs</b></h5>"}}, "534e7dc61af1485eb66ab7d811b9c904": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5efa79b59b0b40a18ca6af43e2316439": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "01065c8ee9664254b422620dc5e4d30c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ceb2734e773438b8d6237a47cc1137a", "style": "IPY_MODEL_b4ea7166ebcd48b2b2aad28de25c6b1b", "value": "<div>Showcasing intersections of features with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "4ceb2734e773438b8d6237a47cc1137a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4ea7166ebcd48b2b2aad28de25c6b1b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d9b23c7927042939f31b9e6401cdf60": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_068074b4de474670b457572e46f28564", "IPY_MODEL_4c083c8ef7d54d87a209fbf367f9ab35"], "layout": "IPY_MODEL_a72391a8a7714bd0a12652bc0076057d"}}, "068074b4de474670b457572e46f28564": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_16bda9a12fca4b029e8b6c20e00df1bd", "style": "IPY_MODEL_1eb4a373d5c54880880322b44bfd1d91", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "16bda9a12fca4b029e8b6c20e00df1bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1eb4a373d5c54880880322b44bfd1d91": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4c083c8ef7d54d87a209fbf367f9ab35": {"model_name": "TabModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "alcohol vs total sulfur dioxide", "1": "total sulfur dioxide vs ", "2": "alcohol vs sulphates", "3": "alcohol vs fixed acidity", "4": "volatile acidity vs total sulfur dioxide"}, "children": ["IPY_MODEL_9ef77b9e1b534d84a705d740de4cb4b1", "IPY_MODEL_e6bb7bf6cdfb4a469711486cb6b233ba", "IPY_MODEL_201b88b0eb7445ffaf323fcf3dfa9b56", "IPY_MODEL_ee402950b03a494cb16f0b81547272c2", "IPY_MODEL_c2db2f96f11545928fc17a36bbe2b4e6"], "layout": "IPY_MODEL_578390ee77de4712b0f397001c176538"}}, "9ef77b9e1b534d84a705d740de4cb4b1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_100cfea4ea4c43eda1dcaee540bb4035", "IPY_MODEL_e91c8b9fa4634062b3f7ceb99158c4ce"], "layout": "IPY_MODEL_4b4a746835824aa69dcd216ea60275b2"}}, "100cfea4ea4c43eda1dcaee540bb4035": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hovertemplate": "alcohol: %{x}<br>total sulfur dioxide: %{y}<br>Neg RMSE score: %{z}<extra></extra>", "name": "0", "text": 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Average Neg RMSE score is -0.5.</div>"}}, "296e269adad44d52ac8482ade498647a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "396be5befff24a5aa1a128ec312f672a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1800b3a142814ecd9f63c8cca1f4b3bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "578390ee77de4712b0f397001c176538": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a72391a8a7714bd0a12652bc0076057d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58ee58f1698a4766bce92f7f7021c39a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7f3b9b95e6974e10a445549a819874eb", "style": "IPY_MODEL_2c36c3dd1d1f42dd8c819b557bee1c2f", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\" style=\"font-size: 14px;\">Go to top</a>"}}, "7f3b9b95e6974e10a445549a819874eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c36c3dd1d1f42dd8c819b557bee1c2f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4316af26c214661a6510d055ccf60b8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "acb2ac3c5a5348c995bda4f5d0b1db7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8940e8ff2c2d4ed0b7bd46445f457e94": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_25ce7548cdab45d6ae17126f0af6ae34", "style": "IPY_MODEL_81a88ecbea0f452d98e5c508147bec28", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "25ce7548cdab45d6ae17126f0af6ae34": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81a88ecbea0f452d98e5c508147bec28": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "24eb4c7f7c86466c968baa74a666eec9": {"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_e096857ffef24620902702f21751f7dd", "IPY_MODEL_8243a79113924087833c7f744008543b", "IPY_MODEL_4d630872fa684c4f8094d613bd41c062", "IPY_MODEL_7eac8585059d4bc79f824588aa552cc1"], "layout": "IPY_MODEL_e4c3a5cafe704aa0aee5a2dbe818052a"}}, "e096857ffef24620902702f21751f7dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0d6ed3b2ec5747b289a29332992a841a", "style": "IPY_MODEL_ddb11fcadbe843ccb39d148f81b8428e", "value": "<h4 id=\"WeakSegmentsPerformance-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Weak Segments Performance - Test Dataset</b></h4>"}}, "0d6ed3b2ec5747b289a29332992a841a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ddb11fcadbe843ccb39d148f81b8428e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8243a79113924087833c7f744008543b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3b86a4fe091a42a8a2645a838817f4e1", "style": "IPY_MODEL_4d534b48337b41d1b8a1ad9c4d2ba2b1", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3b86a4fe091a42a8a2645a838817f4e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d534b48337b41d1b8a1ad9c4d2ba2b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d630872fa684c4f8094d613bd41c062": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_18166348e84e463391eddee880ad2537", "style": "IPY_MODEL_117b1176e34747d79222b0150204f889", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3057e_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3057e_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3057e_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3057e_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3057e_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3057e_\">\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_3057e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_3057e_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_3057e_row0_col2\" class=\"data row0 col2\" >Found a segment with Neg RMSE score of -1.074 in comparison to an average score of -0.602 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "18166348e84e463391eddee880ad2537": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "117b1176e34747d79222b0150204f889": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7eac8585059d4bc79f824588aa552cc1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c8ffd093ba524b6cb9dab3c83657b587", "IPY_MODEL_2421c42e0a0540b69ea7eb916b820ba0", "IPY_MODEL_20a1314525934e83aa8116849e0592ae", "IPY_MODEL_6da47325ac4a4838aa54b004da0b0a3a"], "layout": "IPY_MODEL_4fa56243447a41fcb80c7f53f707f14d"}}, "c8ffd093ba524b6cb9dab3c83657b587": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aac45c24f12943a395ddd051c682755d", "style": "IPY_MODEL_a5bf8d9eb6424b39beb0e0d5d33faa2d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "aac45c24f12943a395ddd051c682755d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a5bf8d9eb6424b39beb0e0d5d33faa2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2421c42e0a0540b69ea7eb916b820ba0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_55b59dd6197e4eee9ea5c85607f9a382", "style": "IPY_MODEL_a4eae26e4a8e439bba7fb00f46961a73", "value": "<div>Showcasing intersections of features with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "55b59dd6197e4eee9ea5c85607f9a382": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a4eae26e4a8e439bba7fb00f46961a73": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "20a1314525934e83aa8116849e0592ae": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_45547047ce3e44c4aba2834f605575d5", "IPY_MODEL_4fcd14a413f448c6a9c0932f6680557f"], "layout": "IPY_MODEL_97a7a6fef6054756bb332b8d262cf295"}}, "45547047ce3e44c4aba2834f605575d5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7907eb1cdc28454fbcfe5bff134bd70f", "style": "IPY_MODEL_241b8d40e5ef46ae932a3240c649ecca", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "7907eb1cdc28454fbcfe5bff134bd70f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "241b8d40e5ef46ae932a3240c649ecca": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4fcd14a413f448c6a9c0932f6680557f": {"model_name": "TabModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "sulphates vs volatile acidity", "1": "alcohol vs sulphates", "2": "total sulfur dioxide vs fixed acidity", "3": "sulphates vs fixed acidity", "4": "volatile acidity vs "}, "children": ["IPY_MODEL_f47de5b438c8463899c4fd93bda03215", 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{"text": "Neg RMSE score (percent of data) volatile acidity vs "}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "title": {"text": "volatile acidity"}}, "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}}, "03b4ce51577e495693b00191717a5fa4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a319442da13e4916af5e01e166efffb4", "style": "IPY_MODEL_b5536883e34d420a8d7cb43857a67423", "value": "<div>Check ran on 320 data samples. 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"layout": "IPY_MODEL_aae6c7d48d114f7f99ad864d93961a5b", "style": "IPY_MODEL_4f537155163c4851b5d15f676c4e1f77", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\" style=\"font-size: 14px;\">Go to top</a>"}}, "aae6c7d48d114f7f99ad864d93961a5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f537155163c4851b5d15f676c4e1f77": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4fa56243447a41fcb80c7f53f707f14d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4c3a5cafe704aa0aee5a2dbe818052a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7463f22c7f1f444783de32c789de0cd7": {"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_378cef450b8444869e8d32764668e205", "IPY_MODEL_ec2df3c7a4214c359cea2f2931f6d712", "IPY_MODEL_49ba9af15f0a40eb91bcbc33a7393a14", "IPY_MODEL_42232cf3afaf4fd39d09199b1d465973"], "layout": "IPY_MODEL_cf21a916c8a24d1ca263ebfb595ad83b"}}, "378cef450b8444869e8d32764668e205": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f9a818b8178e4f8981c7e670841702f8", "style": "IPY_MODEL_f982de27f74545a98c4f02df248ff4df", "value": "<h4 id=\"RegressionSystematicError-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Regression Systematic Error - Test Dataset</b></h4>"}}, "f9a818b8178e4f8981c7e670841702f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f982de27f74545a98c4f02df248ff4df": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ec2df3c7a4214c359cea2f2931f6d712": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_abbce462e93b42a19f49a5fc3f5980cd", "style": "IPY_MODEL_1968e6a04a6d433ea4918f608be0c5a0", "value": "<p>Check the regression systematic error. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_regression_systematic_error.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "abbce462e93b42a19f49a5fc3f5980cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1968e6a04a6d433ea4918f608be0c5a0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49ba9af15f0a40eb91bcbc33a7393a14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a9e1f51815e4beab90f40cbfed5055a", "style": "IPY_MODEL_be9d696f07e34cf592a6e58895961ac2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_63d71_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63d71_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63d71_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63d71_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63d71_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_63d71_\">\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_63d71_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_63d71_row0_col1\" class=\"data row0 col1\" >Bias ratio is less than 0.01</td>\n      <td id=\"T_63d71_row0_col2\" class=\"data row0 col2\" >Found bias ratio 0.05</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7a9e1f51815e4beab90f40cbfed5055a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be9d696f07e34cf592a6e58895961ac2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42232cf3afaf4fd39d09199b1d465973": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_04a253d4d8354730812c4204a0263cca", "IPY_MODEL_8795b3fc06f04a639c981dfcc46dbefa", "IPY_MODEL_4e2f683830db4f6392755819e7388183", "IPY_MODEL_63e8b6f5b5c044b6ab39b9b3b8245960"], "layout": "IPY_MODEL_069ac856ad914f31bf8dfaab0a82c0e4"}}, "04a253d4d8354730812c4204a0263cca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11a99d0076554a4ea564ba5d5cbdad01", "style": "IPY_MODEL_fb228b0fcd564bb7a521112df9e0f112", "value": "<h5><b>Additional Outputs</b></h5>"}}, "11a99d0076554a4ea564ba5d5cbdad01": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb228b0fcd564bb7a521112df9e0f112": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8795b3fc06f04a639c981dfcc46dbefa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ce131eed5844fffbd674d1e073f0fb4", "style": "IPY_MODEL_0c0fea4e9a234f77b8a159607a7fd92f", "value": "<div>Non-zero mean of the error distribution indicated the presents of systematic error in model predictions</div>"}}, "4ce131eed5844fffbd674d1e073f0fb4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0c0fea4e9a234f77b8a159607a7fd92f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4e2f683830db4f6392755819e7388183": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"boxmean": true, "hoverinfo": "x", "name": "Model prediction error", "orientation": "h", "x": {"dtype": "float64", "shape": [320]}, "type": "box", "uid": "36338908-cd28-4880-bcc9-fcbb7e149294"}], 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"63e8b6f5b5c044b6ab39b9b3b8245960": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dc1111d57796475fa1ef4c83cd4630dc", "style": "IPY_MODEL_319d12e1e68a4b7dad32c56930dd86f9", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\" style=\"font-size: 14px;\">Go to top</a>"}}, "dc1111d57796475fa1ef4c83cd4630dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "319d12e1e68a4b7dad32c56930dd86f9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "069ac856ad914f31bf8dfaab0a82c0e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf21a916c8a24d1ca263ebfb595ad83b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "537405ec645c45f89f59c8b813a6e21b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7061760e63bd434b8db65b9a2648d91b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a22c495079bb4a53bca41fa913cb0327": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f380f0deeaa45a6bac5a59a14ecba61": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7f18e07dc2844787a9dd4a68cfe0f130", "IPY_MODEL_f25fe8295eb34d2da62ea07250d315c9"], "layout": "IPY_MODEL_d90a54cf46154c7780666b33262c33e8"}}, "7f18e07dc2844787a9dd4a68cfe0f130": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ed31d1a3ae564fa888da029a9aa639a6", "style": "IPY_MODEL_5726f00d7c7a492988c5d7e17ece3178", "value": "<span id=\"summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"></span>"}}, "ed31d1a3ae564fa888da029a9aa639a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5726f00d7c7a492988c5d7e17ece3178": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f25fe8295eb34d2da62ea07250d315c9": {"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_003149ff796347538db4484325251e70"], "layout": "IPY_MODEL_f5a185a83a1742c38a35bddc3bb31cfd", "selected_index": null}}, "003149ff796347538db4484325251e70": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3e91147c37cd49da9af01ad9ec7c2e82", "IPY_MODEL_6f219d95d5454e51bf779893e6b19f4e", "IPY_MODEL_750ce4d8a2704b3f817f3d2f91daf038", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_c2e517863147451fb969c87d8d2104d1", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_3acbf22f3099498c9e4e9af98d3a58b7", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_f3d9cf05af7f4381b7a1d6f59c458c39", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_bc33fe8f558542dab2dcde2aee40bf8e", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_a1021bf4db8a4a7da52b0baacaa0f9ff", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_849cd499a84d4172b6297a294443100a", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_d7bb82005a89488c929e6e71623f3481", "IPY_MODEL_5ed038002baf4f8c80ac971fc4f3412e", "IPY_MODEL_62fbca60b5d844da9087edb38ffaf653"], "layout": "IPY_MODEL_39cf11b8d0f74181b151ab316b8c4d85"}}, "3e91147c37cd49da9af01ad9ec7c2e82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_00754dc5af1f4de2acd230b81631d197", "style": "IPY_MODEL_2ed6405ced9f491db61992d840140e04", "value": "<style type=\"text/css\">\n#T_3c872_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3c872_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3c872_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3c872_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3c872_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3c872_\">\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_3c872_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestPerformance_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Train Test Performance</a></td>\n      <td id=\"T_3c872_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_3c872_row0_col3\" class=\"data row0 col3\" ></td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row1_col1\" class=\"data row1 col1\" ><a href=\"#TrainTestPredictionDrift_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Train Test Prediction Drift</a></td>\n      <td id=\"T_3c872_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_3c872_row1_col3\" class=\"data row1 col3\" >Found model prediction Earth Mover's Distance drift score of 0.02</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row2_col1\" class=\"data row2 col1\" ><a href=\"#SimpleModelComparison_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Simple Model Comparison</a></td>\n      <td id=\"T_3c872_row2_col2\" class=\"data row2 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_3c872_row2_col3\" class=\"data row2 col3\" >All metrics passed, metric's gain: {'Neg RMSE': '25.79%'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row3_col1\" class=\"data row3 col1\" ><a href=\"#RegressionSystematicError-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Regression Systematic Error - Train Dataset</a></td>\n      <td id=\"T_3c872_row3_col2\" class=\"data row3 col2\" >Bias ratio is less than 0.01</td>\n      <td id=\"T_3c872_row3_col3\" class=\"data row3 col3\" >Found bias ratio 7.96E-16</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row4_col1\" class=\"data row4 col1\" ><a href=\"#RegressionErrorDistribution-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Regression Error Distribution - Train Dataset</a></td>\n      <td id=\"T_3c872_row4_col2\" class=\"data row4 col2\" >Kurtosis value is greater than -0.1</td>\n      <td id=\"T_3c872_row4_col3\" class=\"data row4 col3\" >Found kurtosis value 0.55157</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row5_col1\" class=\"data row5 col1\" ><a href=\"#RegressionErrorDistribution-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Regression Error Distribution - Test Dataset</a></td>\n      <td id=\"T_3c872_row5_col2\" class=\"data row5 col2\" >Kurtosis value is greater than -0.1</td>\n      <td id=\"T_3c872_row5_col3\" class=\"data row5 col3\" >Found kurtosis value 0.47158</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row6_col1\" class=\"data row6 col1\" ><a href=\"#BoostingOverfit_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Boosting Overfit</a></td>\n      <td id=\"T_3c872_row6_col2\" class=\"data row6 col2\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_3c872_row6_col3\" class=\"data row6 col3\" >Found score decline of 0%</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row7_col1\" class=\"data row7 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_3c872_row7_col2\" class=\"data row7 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_3c872_row7_col3\" class=\"data row7 col3\" >Found average inference time (seconds): 2.68e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row8_col1\" class=\"data row8 col1\" ><a href=\"#ModelInferenceTime-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_3c872_row8_col2\" class=\"data row8 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_3c872_row8_col3\" class=\"data row8 col3\" >Found average inference time (seconds): 5.04e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_3c872_row9_col0\" class=\"data row9 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_3c872_row9_col1\" class=\"data row9 col1\" >Unused Features</td>\n      <td id=\"T_3c872_row9_col2\" class=\"data row9 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_3c872_row9_col3\" class=\"data row9 col3\" >Found 0 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "00754dc5af1f4de2acd230b81631d197": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2ed6405ced9f491db61992d840140e04": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6f219d95d5454e51bf779893e6b19f4e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7277686f503e4854be1d57c50ce278ec", "style": "IPY_MODEL_60cd59d049b4443780ea66ab5f4d08f7", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7277686f503e4854be1d57c50ce278ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "60cd59d049b4443780ea66ab5f4d08f7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "750ce4d8a2704b3f817f3d2f91daf038": {"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_c30f7383004b40919fad6cf8719de01d", "IPY_MODEL_3d2e536dfe3c48c89d596b571dc275a4", "IPY_MODEL_418b1c05f237498c9042c009258c6f9f", "IPY_MODEL_ab6bb7ff2679405088aec52906611186"], "layout": "IPY_MODEL_bad05f72f6bc4e0496f3a946be841919"}}, "c30f7383004b40919fad6cf8719de01d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f4287fea5bed4ed1bee8c15875b42884", "style": "IPY_MODEL_e3e79042de28478db31b542adc92a9e4", "value": "<h4 id=\"TrainTestPerformance_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Train Test Performance</b></h4>"}}, "f4287fea5bed4ed1bee8c15875b42884": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e3e79042de28478db31b542adc92a9e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3d2e536dfe3c48c89d596b571dc275a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0b589e2428ca4d479f20a579504e5aea", "style": "IPY_MODEL_d3983b4a5e364c6b94a49d74dceaee0c", "value": "<p>Summarize given model performance on the train and test datasets based on selected scorers. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "0b589e2428ca4d479f20a579504e5aea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3983b4a5e364c6b94a49d74dceaee0c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "418b1c05f237498c9042c009258c6f9f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eed9cce2726346d280862743ef248b83", "style": "IPY_MODEL_2f8e42b228c1482687791680be008370", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_e00cd_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e00cd_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e00cd_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e00cd_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e00cd_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e00cd_\">\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_e00cd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_e00cd_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_e00cd_row0_col2\" class=\"data row0 col2\" ></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "eed9cce2726346d280862743ef248b83": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f8e42b228c1482687791680be008370": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab6bb7ff2679405088aec52906611186": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a6f2f9c2cefb456c8f502a515afd1626", "IPY_MODEL_962c88a028b041528360d2be728d95df", "IPY_MODEL_6bab143d8e514fc4824bc0812b69b98a"], "layout": "IPY_MODEL_916ad4deebb74be5bf320a59b66f3ea2"}}, "a6f2f9c2cefb456c8f502a515afd1626": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_24224f233e204a148b97d46f4d9fbfae", "style": "IPY_MODEL_a14a57ce94f545b7b83e557615a4ac2d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "24224f233e204a148b97d46f4d9fbfae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a14a57ce94f545b7b83e557615a4ac2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "962c88a028b041528360d2be728d95df": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{x}<br>Metric=Neg RMSE<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["Train"], "xaxis": "x", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y", "type": "histogram", "uid": "ea9a94b5-e515-493a-a3c3-835c1656a67b"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{x}<br>Metric=Neg MAE<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["Train"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y2", "type": "histogram", "uid": "9636954d-a41f-47e3-8b62-848c2cb92dac"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{x}<br>Metric=R2<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["Train"], "xaxis": "x3", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y3", "type": "histogram", "uid": "acdfb5ac-2955-4718-bf25-796ceb9df152"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{x}<br>Metric=Neg RMSE<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["Test"], "xaxis": "x", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y", "type": "histogram", "uid": "d2616da1-a633-47ec-89b4-70e210021140"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{x}<br>Metric=Neg MAE<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": ["Test"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y2", "type": "histogram", "uid": "5ec742e2-7b7a-4ffd-85c2-5b15f60f4ac5"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{x}<br>Metric=R2<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": ["Test"], "xaxis": "x3", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y3", "type": "histogram", "uid": "8e68fa9c-c191-487f-bd77-48934cdc5a19"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Neg RMSE", "x": 0.15, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Neg MAE", "x": 0.49999999999999994, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "R2", "x": 0.85, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Dataset"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "categoryarray": ["Train", "Test"], "categoryorder": "array", "domain": [0.0, 0.3], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}, "xaxis2": {"anchor": "y2", "categoryarray": ["Train", "Test"], "categoryorder": "array", "domain": [0.35, 0.6499999999999999], "matches": "x", "type": "category"}, "yaxis2": {"anchor": "x2", "domain": [0.0, 1.0], "showticklabels": true}, "xaxis3": {"anchor": "y3", "categoryarray": ["Train", "Test"], "categoryorder": "array", "domain": [0.7, 1.0], "matches": "x", "type": "category"}, "yaxis3": {"anchor": "x3", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "qalJRxvv378="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "CV+W2gDK2L8="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "+YVg/RHB4z8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "RXZKcVlA478="}, {"encoding": "base64", "path": ["_data", 4, "y", "buffer"], "data": "BU/Y6xcO378="}, {"encoding": "base64", "path": ["_data", 5, "y", "buffer"], "data": "OIBV+xuO3D8="}]}, "6bab143d8e514fc4824bc0812b69b98a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6c687f7287814c59a8e25413ba9f811e", "style": "IPY_MODEL_4e0bb91744a24b9b9f16287bd58372c6", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "6c687f7287814c59a8e25413ba9f811e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4e0bb91744a24b9b9f16287bd58372c6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "916ad4deebb74be5bf320a59b66f3ea2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bad05f72f6bc4e0496f3a946be841919": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ed038002baf4f8c80ac971fc4f3412e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0ce469ed440d4bb7a808dd675ad0a6e0", "style": "IPY_MODEL_c2efe94b29004aa19eedc20fdc00c190", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "0ce469ed440d4bb7a808dd675ad0a6e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2efe94b29004aa19eedc20fdc00c190": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c2e517863147451fb969c87d8d2104d1": {"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_676d7a6c99c14622ae17fe6ccd4f7e29", "IPY_MODEL_778bf0e3ead84ee6a500b9b6053f86ec", "IPY_MODEL_39253afb69af4066940436f6a1fcdd93", "IPY_MODEL_14f0174f657240e1acd7b6f2d1e5df9b"], "layout": "IPY_MODEL_f10daba2b30f4743bd70d87611b1dec4"}}, "676d7a6c99c14622ae17fe6ccd4f7e29": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4518698776f9400b86077e715468aaed", "style": "IPY_MODEL_fb15a9e4126248408c9b070e506690de", "value": "<h4 id=\"TrainTestPredictionDrift_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Train Test Prediction Drift</b></h4>"}}, "4518698776f9400b86077e715468aaed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb15a9e4126248408c9b070e506690de": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "778bf0e3ead84ee6a500b9b6053f86ec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_00055dd5345146b9977b7ac61f578e0b", "style": "IPY_MODEL_775f4fb7d5044eb2b6f15c155bd01b6d", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_train_test_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "00055dd5345146b9977b7ac61f578e0b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "775f4fb7d5044eb2b6f15c155bd01b6d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "39253afb69af4066940436f6a1fcdd93": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9a71bd82dabc4aa19ce995265f55ee1f", "style": "IPY_MODEL_db9bd59a60b9420f819621b2e730852a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_70930_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_70930_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_70930_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_70930_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_70930_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_70930_\">\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_70930_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_70930_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_70930_row0_col2\" class=\"data row0 col2\" >Found model prediction Earth Mover's Distance drift score of 0.02</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "9a71bd82dabc4aa19ce995265f55ee1f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db9bd59a60b9420f819621b2e730852a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "14f0174f657240e1acd7b6f2d1e5df9b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_31ce9b876cc542d8a85a2e694f20cb9e", "IPY_MODEL_3f21d760a798434abbd1d19bd9e7e70f", "IPY_MODEL_06ab0dd85e5c413b859d6104bac81703", "IPY_MODEL_0aac1c5c2231458ca8bfe37633f48ee2"], "layout": "IPY_MODEL_684842f275604e8c8d5f7474dfbb6063"}}, "31ce9b876cc542d8a85a2e694f20cb9e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d0b0bda389ea4837bfe26c0259442a3b", "style": "IPY_MODEL_e88081af23b34113a6372fff085b76d2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d0b0bda389ea4837bfe26c0259442a3b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e88081af23b34113a6372fff085b76d2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3f21d760a798434abbd1d19bd9e7e70f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_83c31ab0e3534977a554d91d4970e0dd", "style": "IPY_MODEL_51946475057d4b56aa2d5d4f6a23b079", "value": "<div><span>\n                The Drift score is a measure for the difference between two distributions, in this check - the test\n                and train distributions.<br> The check shows the drift score and distributions for the predicted\n                classes.\n            </span></div>"}}, "83c31ab0e3534977a554d91d4970e0dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51946475057d4b56aa2d5d4f6a23b079": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "06ab0dd85e5c413b859d6104bac81703": {"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": [0.018493069670473327], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "8e46dad9-7506-43bf-a4cb-d083ab2d889e"}, {"fill": "tozeroy", "line": {"color": "#00008b", "shape": "spline"}, "name": "Train Dataset", "x": [3.4925338775671784, 3.571435201622319, 3.650336525677459, 3.7292378497325998, 3.8081391737877404, 3.8870404978428805, 3.965941821898021, 4.044843145953162, 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"0aac1c5c2231458ca8bfe37633f48ee2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5203918432fa424bab28f5a9f398e4b9", "style": "IPY_MODEL_2c2b2dbd2b1e448a9a8772dcc417447d", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "5203918432fa424bab28f5a9f398e4b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c2b2dbd2b1e448a9a8772dcc417447d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "684842f275604e8c8d5f7474dfbb6063": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f10daba2b30f4743bd70d87611b1dec4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3acbf22f3099498c9e4e9af98d3a58b7": {"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_059becdccc81414c9405cd46aec26a57", "IPY_MODEL_22b0ed0124724b68b7232a32884147f1", "IPY_MODEL_3f32b6e108ec4d85923c9a90a42aeff6", "IPY_MODEL_180c65b69054406293e59853a7e1bfb4"], "layout": "IPY_MODEL_1954a6b718794b758ecd6828dc91feb9"}}, "059becdccc81414c9405cd46aec26a57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2cdc05364d984f2d88d577280654a1c9", "style": "IPY_MODEL_e598aae5507b4d169c498d3459dbc2ab", "value": "<h4 id=\"SimpleModelComparison_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Simple Model Comparison</b></h4>"}}, "2cdc05364d984f2d88d577280654a1c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e598aae5507b4d169c498d3459dbc2ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "22b0ed0124724b68b7232a32884147f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b784c8f0e52c438d9cff67beb62d81db", "style": "IPY_MODEL_3178be67c1104ce789efe9e937e4e685", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b784c8f0e52c438d9cff67beb62d81db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3178be67c1104ce789efe9e937e4e685": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3f32b6e108ec4d85923c9a90a42aeff6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0941ef77887f4abf8d954cbff316ce99", "style": "IPY_MODEL_d5e716f8370b44e7ad5e6e4c6f43d8ce", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f19e4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f19e4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f19e4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f19e4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f19e4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f19e4_\">\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_f19e4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_f19e4_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_f19e4_row0_col2\" class=\"data row0 col2\" >All metrics passed, metric's gain: {'Neg RMSE': '25.79%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0941ef77887f4abf8d954cbff316ce99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d5e716f8370b44e7ad5e6e4c6f43d8ce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "180c65b69054406293e59853a7e1bfb4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e82e2279aa6f4d15b56682fa71c41384", "IPY_MODEL_779c63ec84744506a242c80e9b7e7c32", "IPY_MODEL_024f3f5683f5489cbd52d6e3f7654e04"], "layout": "IPY_MODEL_eb41e4dfe44f4824972d41c550b80c68"}}, "e82e2279aa6f4d15b56682fa71c41384": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11565efa903a460885b4c8873af56b6a", "style": "IPY_MODEL_ad6061b0c9074bd7904a1f182bb78cc8", "value": "<h5><b>Additional Outputs</b></h5>"}}, "11565efa903a460885b4c8873af56b6a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad6061b0c9074bd7904a1f182bb78cc8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "779c63ec84744506a242c80e9b7e7c32": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=%{x}<br>Metric=Neg RMSE<br>sum of Value=%{y}<extra></extra>", "legendgroup": "GradientBoostingRegressor model", "marker": {"color": "#636efa", "pattern": {"shape": ""}}, "name": "GradientBoostingRegressor model", "offsetgroup": "GradientBoostingRegressor model", "orientation": "v", "showlegend": true, "x": ["GradientBoostingRegressor model"], "xaxis": "x", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y", "type": "histogram", "uid": "55739c70-847c-4342-ae21-a3cb5aa4effd"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=%{x}<br>Metric=Neg RMSE<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple model - most_frequent", "marker": {"color": "#EF553B", "pattern": {"shape": ""}}, "name": "Simple model - most_frequent", "offsetgroup": "Simple model - most_frequent", "orientation": "v", "showlegend": true, "x": ["Simple model - most_frequent"], "xaxis": "x", "y": {"dtype": "float64", "shape": [1]}, "yaxis": "y", "type": "histogram", "uid": "226f038f-ee31-46a5-b95b-8134bff50b23"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Neg RMSE", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "categoryarray": ["GradientBoostingRegressor model", "Simple model - most_frequent"], "categoryorder": "array", "domain": [0.0, 1.0]}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "RXZKcVlA478="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "mP6w3eDw6b8="}]}, "024f3f5683f5489cbd52d6e3f7654e04": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ed430e9638d6487084a5ccd5099a7d39", "style": "IPY_MODEL_bfab08ff6bea40f599468a6bec9d4589", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "ed430e9638d6487084a5ccd5099a7d39": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bfab08ff6bea40f599468a6bec9d4589": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eb41e4dfe44f4824972d41c550b80c68": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1954a6b718794b758ecd6828dc91feb9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f3d9cf05af7f4381b7a1d6f59c458c39": {"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_b48d6e8722e7405c8992e493afbe0069", "IPY_MODEL_62f221ebad83413ba0c3b90bfe8bda1f", "IPY_MODEL_6faba90a282a481b88cab0fa25c60607", "IPY_MODEL_7fbb7e80e4d9473d9f863ed05d966178"], "layout": "IPY_MODEL_8942d3937524425d84f0c6c5396ff8aa"}}, "b48d6e8722e7405c8992e493afbe0069": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3dbc53e6bcb94375871a6954069acaae", "style": "IPY_MODEL_5c8e9a0b759d48cbac4a9d51c05ead4c", "value": "<h4 id=\"RegressionSystematicError-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Regression Systematic Error - Train Dataset</b></h4>"}}, "3dbc53e6bcb94375871a6954069acaae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5c8e9a0b759d48cbac4a9d51c05ead4c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "62f221ebad83413ba0c3b90bfe8bda1f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e5697fac707242da9bd07627b0f4b3f2", "style": "IPY_MODEL_48cb74c86b074e8793a8bf6038631451", "value": "<p>Check the regression systematic error. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_regression_systematic_error.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e5697fac707242da9bd07627b0f4b3f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "48cb74c86b074e8793a8bf6038631451": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6faba90a282a481b88cab0fa25c60607": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b7a86dcda2bc4a21803f9cb3a1874589", "style": "IPY_MODEL_753e47574cc542c79f28dc9740da676f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8b454_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b454_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b454_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b454_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8b454_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8b454_\">\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_8b454_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_8b454_row0_col1\" class=\"data row0 col1\" >Bias ratio is less than 0.01</td>\n      <td id=\"T_8b454_row0_col2\" class=\"data row0 col2\" >Found bias ratio 7.96E-16</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b7a86dcda2bc4a21803f9cb3a1874589": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "753e47574cc542c79f28dc9740da676f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7fbb7e80e4d9473d9f863ed05d966178": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", 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"}]}, "bf672586b4a641d7bd4d8c74ad342103": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fe5ca759b35749ea8a7a9d87edc70e1c", "style": "IPY_MODEL_95dab2c96d864226ba72fc72bb2b4f94", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "fe5ca759b35749ea8a7a9d87edc70e1c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95dab2c96d864226ba72fc72bb2b4f94": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da5f2aa3dc534dbda9e1b276574f3e89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8942d3937524425d84f0c6c5396ff8aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc33fe8f558542dab2dcde2aee40bf8e": {"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_df7794d9f31b469b8a01133e4f87ac86", "IPY_MODEL_edd17baaebfb451bb41a032f48bf1907", "IPY_MODEL_57ddad9b8246405d9974abb4b35eb783", "IPY_MODEL_45a0a222a0114119bbbf880532ccae9d"], "layout": "IPY_MODEL_a87927002b5c4e99a5f63b9e577246c4"}}, "df7794d9f31b469b8a01133e4f87ac86": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aeb3811f51aa4e31b54e1a27b643f59d", "style": "IPY_MODEL_6fc6055ce46e4ef29e8b381df8dc8d4d", "value": "<h4 id=\"RegressionErrorDistribution-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Regression Error Distribution - Train Dataset</b></h4>"}}, "aeb3811f51aa4e31b54e1a27b643f59d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6fc6055ce46e4ef29e8b381df8dc8d4d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "edd17baaebfb451bb41a032f48bf1907": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6918740cc04b46ac9b11742cba84e0aa", "style": "IPY_MODEL_a3c273211d4a4ca7aec6050119b1a182", "value": "<p>Check regression error distribution. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_regression_error_distribution.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6918740cc04b46ac9b11742cba84e0aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a3c273211d4a4ca7aec6050119b1a182": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57ddad9b8246405d9974abb4b35eb783": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5ab4e5360081495f835ea9b9b4a849f0", "style": "IPY_MODEL_da9730840354496184160d74d27f60ad", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_00576_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_00576_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_00576_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_00576_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_00576_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_00576_\">\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_00576_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_00576_row0_col1\" class=\"data row0 col1\" >Kurtosis value is greater than -0.1</td>\n      <td id=\"T_00576_row0_col2\" class=\"data row0 col2\" >Found kurtosis value 0.55157</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5ab4e5360081495f835ea9b9b4a849f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da9730840354496184160d74d27f60ad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45a0a222a0114119bbbf880532ccae9d": {"model_name": "VBoxModel", "model_module": 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"}]}, "ff5866da27dd493ba110462febb3a110": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ea3d3eb626247dc8b4236feec4fd4ac", "style": "IPY_MODEL_485aa404b68e47f89239e12d1beb230d", "value": "<div>Largest over estimation errors:</div>"}}, "4ea3d3eb626247dc8b4236feec4fd4ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "485aa404b68e47f89239e12d1beb230d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eefadc410fe04057aad96a6d7e475f09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1eafc0cfdfcf4572b8a6eddc092858d7", "style": "IPY_MODEL_a367af299216422d80591903697f526c", "value": "<style type=\"text/css\">\n#T_08254_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_08254_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_08254_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_08254_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_08254_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_08254_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >fixed acidity</th>\n      <th class=\"col_heading level0 col1\" >volatile acidity</th>\n      <th class=\"col_heading level0 col2\" >citric acid</th>\n      <th class=\"col_heading level0 col3\" >residual sugar</th>\n      <th class=\"col_heading level0 col4\" >chlorides</th>\n      <th class=\"col_heading level0 col5\" >free sulfur dioxide</th>\n      <th class=\"col_heading level0 col6\" >total sulfur dioxide</th>\n      <th class=\"col_heading level0 col7\" >density</th>\n      <th class=\"col_heading level0 col8\" >pH</th>\n      <th class=\"col_heading level0 col9\" >sulphates</th>\n      <th class=\"col_heading level0 col10\" >alcohol</th>\n      <th class=\"col_heading level0 col11\" >quality</th>\n      <th class=\"col_heading level0 col12\" >predicted quality</th>\n      <th class=\"col_heading level0 col13\" >quality Prediction Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_08254_level0_row0\" class=\"row_heading level0 row0\" >7</th>\n      <td id=\"T_08254_row0_col0\" class=\"data row0 col0\" >7.30</td>\n      <td id=\"T_08254_row0_col1\" class=\"data row0 col1\" >0.65</td>\n      <td id=\"T_08254_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_08254_row0_col3\" class=\"data row0 col3\" >1.20</td>\n      <td id=\"T_08254_row0_col4\" class=\"data row0 col4\" >0.07</td>\n      <td id=\"T_08254_row0_col5\" class=\"data row0 col5\" >15.00</td>\n      <td id=\"T_08254_row0_col6\" class=\"data row0 col6\" >21.00</td>\n      <td id=\"T_08254_row0_col7\" class=\"data row0 col7\" >0.99</td>\n      <td id=\"T_08254_row0_col8\" class=\"data row0 col8\" >3.39</td>\n      <td id=\"T_08254_row0_col9\" class=\"data row0 col9\" >0.47</td>\n      <td id=\"T_08254_row0_col10\" class=\"data row0 col10\" >10.00</td>\n      <td id=\"T_08254_row0_col11\" class=\"data row0 col11\" >7</td>\n      <td id=\"T_08254_row0_col12\" class=\"data row0 col12\" >5.37</td>\n      <td id=\"T_08254_row0_col13\" class=\"data row0 col13\" >1.63</td>\n    </tr>\n    <tr>\n      <th id=\"T_08254_level0_row1\" class=\"row_heading level0 row1\" >8</th>\n      <td id=\"T_08254_row1_col0\" class=\"data row1 col0\" >7.80</td>\n      <td id=\"T_08254_row1_col1\" class=\"data row1 col1\" >0.58</td>\n      <td id=\"T_08254_row1_col2\" class=\"data row1 col2\" >0.02</td>\n      <td id=\"T_08254_row1_col3\" class=\"data row1 col3\" >2.00</td>\n      <td id=\"T_08254_row1_col4\" class=\"data row1 col4\" >0.07</td>\n      <td id=\"T_08254_row1_col5\" class=\"data row1 col5\" >9.00</td>\n      <td id=\"T_08254_row1_col6\" class=\"data row1 col6\" >18.00</td>\n      <td id=\"T_08254_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_08254_row1_col8\" class=\"data row1 col8\" >3.36</td>\n      <td id=\"T_08254_row1_col9\" class=\"data row1 col9\" >0.57</td>\n      <td id=\"T_08254_row1_col10\" class=\"data row1 col10\" >9.50</td>\n      <td id=\"T_08254_row1_col11\" class=\"data row1 col11\" >7</td>\n      <td id=\"T_08254_row1_col12\" class=\"data row1 col12\" >5.44</td>\n      <td id=\"T_08254_row1_col13\" class=\"data row1 col13\" >1.56</td>\n    </tr>\n    <tr>\n      <th id=\"T_08254_level0_row2\" class=\"row_heading level0 row2\" >1403</th>\n      <td id=\"T_08254_row2_col0\" class=\"data row2 col0\" >7.20</td>\n      <td id=\"T_08254_row2_col1\" class=\"data row2 col1\" >0.33</td>\n      <td id=\"T_08254_row2_col2\" class=\"data row2 col2\" >0.33</td>\n      <td id=\"T_08254_row2_col3\" class=\"data row2 col3\" >1.70</td>\n      <td id=\"T_08254_row2_col4\" class=\"data row2 col4\" >0.06</td>\n      <td id=\"T_08254_row2_col5\" class=\"data row2 col5\" >3.00</td>\n      <td id=\"T_08254_row2_col6\" class=\"data row2 col6\" >13.00</td>\n      <td id=\"T_08254_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_08254_row2_col8\" class=\"data row2 col8\" >3.23</td>\n      <td id=\"T_08254_row2_col9\" class=\"data row2 col9\" >1.10</td>\n      <td id=\"T_08254_row2_col10\" class=\"data row2 col10\" >10.00</td>\n      <td id=\"T_08254_row2_col11\" class=\"data row2 col11\" >8</td>\n      <td id=\"T_08254_row2_col12\" class=\"data row2 col12\" >6.44</td>\n      <td id=\"T_08254_row2_col13\" class=\"data row2 col13\" >1.56</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1eafc0cfdfcf4572b8a6eddc092858d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a367af299216422d80591903697f526c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "488464ffdc3e4fcbabb1f375fa41af0c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3dcb0e9486d546cd89f874eaf24ac82d", "style": "IPY_MODEL_ba9984999a17432887c54c8aee7d6bc9", "value": "<div>Largest under estimation errors:</div>"}}, "3dcb0e9486d546cd89f874eaf24ac82d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ba9984999a17432887c54c8aee7d6bc9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "15b6dde15c234395821bd9e0b2be6f00": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c05b1dc54e7d41c3af79612c9c5285bd", "style": "IPY_MODEL_0f7f618d303946d1849e5a36bfc9d3cd", "value": "<style type=\"text/css\">\n#T_6e1dc_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e1dc_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e1dc_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e1dc_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6e1dc_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6e1dc_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >fixed acidity</th>\n      <th class=\"col_heading level0 col1\" >volatile acidity</th>\n      <th class=\"col_heading level0 col2\" >citric acid</th>\n      <th class=\"col_heading level0 col3\" >residual sugar</th>\n      <th class=\"col_heading level0 col4\" >chlorides</th>\n      <th class=\"col_heading level0 col5\" >free sulfur dioxide</th>\n      <th class=\"col_heading level0 col6\" >total sulfur dioxide</th>\n      <th class=\"col_heading level0 col7\" >density</th>\n      <th class=\"col_heading level0 col8\" >pH</th>\n      <th class=\"col_heading level0 col9\" >sulphates</th>\n      <th class=\"col_heading level0 col10\" >alcohol</th>\n      <th class=\"col_heading level0 col11\" >quality</th>\n      <th class=\"col_heading level0 col12\" >predicted quality</th>\n      <th class=\"col_heading level0 col13\" >quality Prediction Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6e1dc_level0_row0\" class=\"row_heading level0 row0\" >1469</th>\n      <td id=\"T_6e1dc_row0_col0\" class=\"data row0 col0\" >7.30</td>\n      <td id=\"T_6e1dc_row0_col1\" class=\"data row0 col1\" >0.98</td>\n      <td id=\"T_6e1dc_row0_col2\" class=\"data row0 col2\" >0.05</td>\n      <td id=\"T_6e1dc_row0_col3\" class=\"data row0 col3\" >2.10</td>\n      <td id=\"T_6e1dc_row0_col4\" class=\"data row0 col4\" >0.06</td>\n      <td id=\"T_6e1dc_row0_col5\" class=\"data row0 col5\" >20.00</td>\n      <td id=\"T_6e1dc_row0_col6\" class=\"data row0 col6\" >49.00</td>\n      <td id=\"T_6e1dc_row0_col7\" class=\"data row0 col7\" >1.00</td>\n      <td id=\"T_6e1dc_row0_col8\" class=\"data row0 col8\" >3.31</td>\n      <td id=\"T_6e1dc_row0_col9\" class=\"data row0 col9\" >0.55</td>\n      <td id=\"T_6e1dc_row0_col10\" class=\"data row0 col10\" >9.70</td>\n      <td id=\"T_6e1dc_row0_col11\" class=\"data row0 col11\" >3</td>\n      <td id=\"T_6e1dc_row0_col12\" class=\"data row0 col12\" >5.02</td>\n      <td id=\"T_6e1dc_row0_col13\" class=\"data row0 col13\" >-2.02</td>\n    </tr>\n    <tr>\n      <th id=\"T_6e1dc_level0_row1\" class=\"row_heading level0 row1\" >1478</th>\n      <td id=\"T_6e1dc_row1_col0\" class=\"data row1 col0\" >7.10</td>\n      <td id=\"T_6e1dc_row1_col1\" class=\"data row1 col1\" >0.88</td>\n      <td id=\"T_6e1dc_row1_col2\" class=\"data row1 col2\" >0.05</td>\n      <td id=\"T_6e1dc_row1_col3\" class=\"data row1 col3\" >5.70</td>\n      <td id=\"T_6e1dc_row1_col4\" class=\"data row1 col4\" >0.08</td>\n      <td id=\"T_6e1dc_row1_col5\" class=\"data row1 col5\" >3.00</td>\n      <td id=\"T_6e1dc_row1_col6\" class=\"data row1 col6\" >14.00</td>\n      <td id=\"T_6e1dc_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_6e1dc_row1_col8\" class=\"data row1 col8\" >3.40</td>\n      <td id=\"T_6e1dc_row1_col9\" class=\"data row1 col9\" >0.52</td>\n      <td id=\"T_6e1dc_row1_col10\" class=\"data row1 col10\" >10.20</td>\n      <td id=\"T_6e1dc_row1_col11\" class=\"data row1 col11\" >3</td>\n      <td id=\"T_6e1dc_row1_col12\" class=\"data row1 col12\" >4.58</td>\n      <td id=\"T_6e1dc_row1_col13\" class=\"data row1 col13\" >-1.58</td>\n    </tr>\n    <tr>\n      <th id=\"T_6e1dc_level0_row2\" class=\"row_heading level0 row2\" >1423</th>\n      <td id=\"T_6e1dc_row2_col0\" class=\"data row2 col0\" >6.40</td>\n      <td id=\"T_6e1dc_row2_col1\" class=\"data row2 col1\" >0.53</td>\n      <td id=\"T_6e1dc_row2_col2\" class=\"data row2 col2\" >0.09</td>\n      <td id=\"T_6e1dc_row2_col3\" class=\"data row2 col3\" >3.90</td>\n      <td id=\"T_6e1dc_row2_col4\" class=\"data row2 col4\" >0.12</td>\n      <td id=\"T_6e1dc_row2_col5\" class=\"data row2 col5\" >14.00</td>\n      <td id=\"T_6e1dc_row2_col6\" class=\"data row2 col6\" >31.00</td>\n      <td id=\"T_6e1dc_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_6e1dc_row2_col8\" class=\"data row2 col8\" >3.50</td>\n      <td id=\"T_6e1dc_row2_col9\" class=\"data row2 col9\" >0.67</td>\n      <td id=\"T_6e1dc_row2_col10\" class=\"data row2 col10\" >11.00</td>\n      <td id=\"T_6e1dc_row2_col11\" class=\"data row2 col11\" >4</td>\n      <td id=\"T_6e1dc_row2_col12\" class=\"data row2 col12\" >5.57</td>\n      <td id=\"T_6e1dc_row2_col13\" class=\"data row2 col13\" >-1.57</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c05b1dc54e7d41c3af79612c9c5285bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0f7f618d303946d1849e5a36bfc9d3cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1bd99664a9ea4f3aa5973ad166f7951a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0cbadac881224c51bf095bdd20ec8a71", "style": "IPY_MODEL_941f1d116b29430195bc6e9a80458993", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "0cbadac881224c51bf095bdd20ec8a71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "941f1d116b29430195bc6e9a80458993": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d32c490861b0464484d149f0d5aa6670": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a87927002b5c4e99a5f63b9e577246c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a1021bf4db8a4a7da52b0baacaa0f9ff": {"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_f3b1b99ae3784c6a8c32e3afa0ce59e3", "IPY_MODEL_82fce16dd188415daa0181054277e30c", "IPY_MODEL_fa0632543cce49c686c63816d03eaee7", "IPY_MODEL_b5e85ba93d814c80a918066c9bc9c0bc"], "layout": "IPY_MODEL_883d5fa099984e6da1a89e8be5e5dfed"}}, "f3b1b99ae3784c6a8c32e3afa0ce59e3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b6cbe087385a44c981fb08eb8e5ed8c9", "style": "IPY_MODEL_c3b5ea4b02474ad5a8b21d8df9733b69", "value": "<h4 id=\"RegressionErrorDistribution-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Regression Error Distribution - Test Dataset</b></h4>"}}, "b6cbe087385a44c981fb08eb8e5ed8c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3b5ea4b02474ad5a8b21d8df9733b69": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "82fce16dd188415daa0181054277e30c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d3872d8e3ee04f49949cf210411bdb9b", "style": "IPY_MODEL_94a49723c2a04d188be4593d0c0e37e4", "value": "<p>Check regression error distribution. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_regression_error_distribution.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d3872d8e3ee04f49949cf210411bdb9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "94a49723c2a04d188be4593d0c0e37e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa0632543cce49c686c63816d03eaee7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_299f29d05cfd48efb43225b511c915e0", "style": "IPY_MODEL_52913f1a2b6c45b28c04ba77258c3a74", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_71556_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_71556_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_71556_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_71556_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_71556_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_71556_\">\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_71556_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_71556_row0_col1\" class=\"data row0 col1\" >Kurtosis value is greater than -0.1</td>\n      <td id=\"T_71556_row0_col2\" class=\"data row0 col2\" >Found kurtosis value 0.47158</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "299f29d05cfd48efb43225b511c915e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52913f1a2b6c45b28c04ba77258c3a74": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b5e85ba93d814c80a918066c9bc9c0bc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2aedba081b074f58ab837b17e2bec4dc", "IPY_MODEL_fcac9cd06dd449e7b4ce103618406591", "IPY_MODEL_1186449a9f4b44fb9e45d9d28c74de7c", "IPY_MODEL_12a1adacaef5473094332cde603bfa37", "IPY_MODEL_777e959f9bdd49a6aa921dd49aa201a9", "IPY_MODEL_51518308b5d74da5afbff09380e7ab38", "IPY_MODEL_8f16e4f732404baa9e61ce120c991759"], "layout": "IPY_MODEL_d9d3a66c9647453aa0e02b5aab933987"}}, "2aedba081b074f58ab837b17e2bec4dc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_83819528cd6249b4a20fbce499a51612", "style": "IPY_MODEL_58ab22a0f8d344cbab3d3bd21142eac3", "value": "<h5><b>Additional Outputs</b></h5>"}}, "83819528cd6249b4a20fbce499a51612": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58ab22a0f8d344cbab3d3bd21142eac3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fcac9cd06dd449e7b4ce103618406591": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "hovertemplate": "quality prediction error=%{x}<br>count=%{y}<extra></extra>", "legendgroup": "", "marker": {"color": "#636efa", "pattern": {"shape": ""}}, "name": "", "nbinsx": 40, "offsetgroup": "", "orientation": "v", "showlegend": false, "x": {"dtype": "float64", "shape": [320]}, "xaxis": "x", "yaxis": "y", "type": "histogram", "uid": "8db744b3-7075-44fc-a153-5d3bbcae6e70"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "relative", "height": 500, "legend": {"tracegroupgap": 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": 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"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": "Histogram of prediction errors"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "title": {"text": "quality prediction error"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "count"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "x", "buffer"], "data": 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"1186449a9f4b44fb9e45d9d28c74de7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2487561bf7744b9585b66ae1dd22c499", "style": "IPY_MODEL_7a5ffb14992548b8b4b634be4ae99148", "value": "<div>Largest over estimation errors:</div>"}}, "2487561bf7744b9585b66ae1dd22c499": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a5ffb14992548b8b4b634be4ae99148": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12a1adacaef5473094332cde603bfa37": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3c90dc85379549ab99d14dd8aa876749", "style": "IPY_MODEL_db59584bccbd4691b162f788b893f549", "value": "<style type=\"text/css\">\n#T_a4aee_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4aee_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4aee_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4aee_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a4aee_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a4aee_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >fixed acidity</th>\n      <th class=\"col_heading level0 col1\" >volatile acidity</th>\n      <th class=\"col_heading level0 col2\" >citric acid</th>\n      <th class=\"col_heading level0 col3\" >residual sugar</th>\n      <th class=\"col_heading level0 col4\" >chlorides</th>\n      <th class=\"col_heading level0 col5\" >free sulfur dioxide</th>\n      <th class=\"col_heading level0 col6\" >total sulfur dioxide</th>\n      <th class=\"col_heading level0 col7\" >density</th>\n      <th class=\"col_heading level0 col8\" >pH</th>\n      <th class=\"col_heading level0 col9\" >sulphates</th>\n      <th class=\"col_heading level0 col10\" >alcohol</th>\n      <th class=\"col_heading level0 col11\" >quality</th>\n      <th class=\"col_heading level0 col12\" >predicted quality</th>\n      <th class=\"col_heading level0 col13\" >quality Prediction Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a4aee_level0_row0\" class=\"row_heading level0 row0\" >1449</th>\n      <td id=\"T_a4aee_row0_col0\" class=\"data row0 col0\" >7.20</td>\n      <td id=\"T_a4aee_row0_col1\" class=\"data row0 col1\" >0.38</td>\n      <td id=\"T_a4aee_row0_col2\" class=\"data row0 col2\" >0.31</td>\n      <td id=\"T_a4aee_row0_col3\" class=\"data row0 col3\" >2.00</td>\n      <td id=\"T_a4aee_row0_col4\" class=\"data row0 col4\" >0.06</td>\n      <td id=\"T_a4aee_row0_col5\" class=\"data row0 col5\" >15.00</td>\n      <td id=\"T_a4aee_row0_col6\" class=\"data row0 col6\" >29.00</td>\n      <td id=\"T_a4aee_row0_col7\" class=\"data row0 col7\" >0.99</td>\n      <td id=\"T_a4aee_row0_col8\" class=\"data row0 col8\" >3.23</td>\n      <td id=\"T_a4aee_row0_col9\" class=\"data row0 col9\" >0.76</td>\n      <td id=\"T_a4aee_row0_col10\" class=\"data row0 col10\" >11.30</td>\n      <td id=\"T_a4aee_row0_col11\" class=\"data row0 col11\" >8</td>\n      <td id=\"T_a4aee_row0_col12\" class=\"data row0 col12\" >6.28</td>\n      <td id=\"T_a4aee_row0_col13\" class=\"data row0 col13\" >1.72</td>\n    </tr>\n    <tr>\n      <th id=\"T_a4aee_level0_row1\" class=\"row_heading level0 row1\" >1269</th>\n      <td id=\"T_a4aee_row1_col0\" class=\"data row1 col0\" >5.50</td>\n      <td id=\"T_a4aee_row1_col1\" class=\"data row1 col1\" >0.49</td>\n      <td id=\"T_a4aee_row1_col2\" class=\"data row1 col2\" >0.03</td>\n      <td id=\"T_a4aee_row1_col3\" class=\"data row1 col3\" >1.80</td>\n      <td id=\"T_a4aee_row1_col4\" class=\"data row1 col4\" >0.04</td>\n      <td id=\"T_a4aee_row1_col5\" class=\"data row1 col5\" >28.00</td>\n      <td id=\"T_a4aee_row1_col6\" class=\"data row1 col6\" >87.00</td>\n      <td id=\"T_a4aee_row1_col7\" class=\"data row1 col7\" >0.99</td>\n      <td id=\"T_a4aee_row1_col8\" class=\"data row1 col8\" >3.50</td>\n      <td id=\"T_a4aee_row1_col9\" class=\"data row1 col9\" >0.82</td>\n      <td id=\"T_a4aee_row1_col10\" class=\"data row1 col10\" >14.00</td>\n      <td id=\"T_a4aee_row1_col11\" class=\"data row1 col11\" >8</td>\n      <td id=\"T_a4aee_row1_col12\" class=\"data row1 col12\" >6.48</td>\n      <td id=\"T_a4aee_row1_col13\" class=\"data row1 col13\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_a4aee_level0_row2\" class=\"row_heading level0 row2\" >1436</th>\n      <td id=\"T_a4aee_row2_col0\" class=\"data row2 col0\" >10.00</td>\n      <td id=\"T_a4aee_row2_col1\" class=\"data row2 col1\" >0.38</td>\n      <td id=\"T_a4aee_row2_col2\" class=\"data row2 col2\" >0.38</td>\n      <td id=\"T_a4aee_row2_col3\" class=\"data row2 col3\" >1.60</td>\n      <td id=\"T_a4aee_row2_col4\" class=\"data row2 col4\" >0.17</td>\n      <td id=\"T_a4aee_row2_col5\" class=\"data row2 col5\" >27.00</td>\n      <td id=\"T_a4aee_row2_col6\" class=\"data row2 col6\" >90.00</td>\n      <td id=\"T_a4aee_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_a4aee_row2_col8\" class=\"data row2 col8\" >3.15</td>\n      <td id=\"T_a4aee_row2_col9\" class=\"data row2 col9\" >0.65</td>\n      <td id=\"T_a4aee_row2_col10\" class=\"data row2 col10\" >8.50</td>\n      <td id=\"T_a4aee_row2_col11\" class=\"data row2 col11\" >5</td>\n      <td id=\"T_a4aee_row2_col12\" class=\"data row2 col12\" >3.52</td>\n      <td id=\"T_a4aee_row2_col13\" class=\"data row2 col13\" >1.48</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3c90dc85379549ab99d14dd8aa876749": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db59584bccbd4691b162f788b893f549": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "777e959f9bdd49a6aa921dd49aa201a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7015f5b509344a7980b3bf3ba33b353c", "style": "IPY_MODEL_e9205b1fb019495987f076610abee47b", "value": "<div>Largest under estimation errors:</div>"}}, "7015f5b509344a7980b3bf3ba33b353c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e9205b1fb019495987f076610abee47b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "51518308b5d74da5afbff09380e7ab38": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58ce89f6536344efa43bd1d8ae85a04b", "style": "IPY_MODEL_882be578ac6041dbbb307d0989b67ec0", "value": "<style type=\"text/css\">\n#T_44019_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_44019_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_44019_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_44019_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_44019_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_44019_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >fixed acidity</th>\n      <th class=\"col_heading level0 col1\" >volatile acidity</th>\n      <th class=\"col_heading level0 col2\" >citric acid</th>\n      <th class=\"col_heading level0 col3\" >residual sugar</th>\n      <th class=\"col_heading level0 col4\" >chlorides</th>\n      <th class=\"col_heading level0 col5\" >free sulfur dioxide</th>\n      <th class=\"col_heading level0 col6\" >total sulfur dioxide</th>\n      <th class=\"col_heading level0 col7\" >density</th>\n      <th class=\"col_heading level0 col8\" >pH</th>\n      <th class=\"col_heading level0 col9\" >sulphates</th>\n      <th class=\"col_heading level0 col10\" >alcohol</th>\n      <th class=\"col_heading level0 col11\" >quality</th>\n      <th class=\"col_heading level0 col12\" >predicted quality</th>\n      <th class=\"col_heading level0 col13\" >quality Prediction Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_44019_level0_row0\" class=\"row_heading level0 row0\" >1505</th>\n      <td id=\"T_44019_row0_col0\" class=\"data row0 col0\" >6.70</td>\n      <td id=\"T_44019_row0_col1\" class=\"data row0 col1\" >0.76</td>\n      <td id=\"T_44019_row0_col2\" class=\"data row0 col2\" >0.02</td>\n      <td id=\"T_44019_row0_col3\" class=\"data row0 col3\" >1.80</td>\n      <td id=\"T_44019_row0_col4\" class=\"data row0 col4\" >0.08</td>\n      <td id=\"T_44019_row0_col5\" class=\"data row0 col5\" >6.00</td>\n      <td id=\"T_44019_row0_col6\" class=\"data row0 col6\" >12.00</td>\n      <td id=\"T_44019_row0_col7\" class=\"data row0 col7\" >1.00</td>\n      <td id=\"T_44019_row0_col8\" class=\"data row0 col8\" >3.55</td>\n      <td id=\"T_44019_row0_col9\" class=\"data row0 col9\" >0.63</td>\n      <td id=\"T_44019_row0_col10\" class=\"data row0 col10\" >9.95</td>\n      <td id=\"T_44019_row0_col11\" class=\"data row0 col11\" >3</td>\n      <td id=\"T_44019_row0_col12\" class=\"data row0 col12\" >5.46</td>\n      <td id=\"T_44019_row0_col13\" class=\"data row0 col13\" >-2.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_44019_level0_row1\" class=\"row_heading level0 row1\" >462</th>\n      <td id=\"T_44019_row1_col0\" class=\"data row1 col0\" >11.00</td>\n      <td id=\"T_44019_row1_col1\" class=\"data row1 col1\" >0.26</td>\n      <td id=\"T_44019_row1_col2\" class=\"data row1 col2\" >0.68</td>\n      <td id=\"T_44019_row1_col3\" class=\"data row1 col3\" >2.55</td>\n      <td id=\"T_44019_row1_col4\" class=\"data row1 col4\" >0.09</td>\n      <td id=\"T_44019_row1_col5\" class=\"data row1 col5\" >10.00</td>\n      <td id=\"T_44019_row1_col6\" class=\"data row1 col6\" >25.00</td>\n      <td id=\"T_44019_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_44019_row1_col8\" class=\"data row1 col8\" >3.18</td>\n      <td id=\"T_44019_row1_col9\" class=\"data row1 col9\" >0.61</td>\n      <td id=\"T_44019_row1_col10\" class=\"data row1 col10\" >11.80</td>\n      <td id=\"T_44019_row1_col11\" class=\"data row1 col11\" >5</td>\n      <td id=\"T_44019_row1_col12\" class=\"data row1 col12\" >6.76</td>\n      <td id=\"T_44019_row1_col13\" class=\"data row1 col13\" >-1.76</td>\n    </tr>\n    <tr>\n      <th id=\"T_44019_level0_row2\" class=\"row_heading level0 row2\" >1484</th>\n      <td id=\"T_44019_row2_col0\" class=\"data row2 col0\" >6.80</td>\n      <td id=\"T_44019_row2_col1\" class=\"data row2 col1\" >0.91</td>\n      <td id=\"T_44019_row2_col2\" class=\"data row2 col2\" >0.06</td>\n      <td id=\"T_44019_row2_col3\" class=\"data row2 col3\" >2.00</td>\n      <td id=\"T_44019_row2_col4\" class=\"data row2 col4\" >0.06</td>\n      <td id=\"T_44019_row2_col5\" class=\"data row2 col5\" >4.00</td>\n      <td id=\"T_44019_row2_col6\" class=\"data row2 col6\" >11.00</td>\n      <td id=\"T_44019_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_44019_row2_col8\" class=\"data row2 col8\" >3.53</td>\n      <td id=\"T_44019_row2_col9\" class=\"data row2 col9\" >0.64</td>\n      <td id=\"T_44019_row2_col10\" class=\"data row2 col10\" >10.90</td>\n      <td id=\"T_44019_row2_col11\" class=\"data row2 col11\" >4</td>\n      <td id=\"T_44019_row2_col12\" class=\"data row2 col12\" >5.62</td>\n      <td id=\"T_44019_row2_col13\" class=\"data row2 col13\" >-1.62</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "58ce89f6536344efa43bd1d8ae85a04b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "882be578ac6041dbbb307d0989b67ec0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8f16e4f732404baa9e61ce120c991759": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_42f135f10e784c9c9763f03f181f79d0", "style": "IPY_MODEL_8276301ecd624275b5be3492b8c46777", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "42f135f10e784c9c9763f03f181f79d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8276301ecd624275b5be3492b8c46777": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d9d3a66c9647453aa0e02b5aab933987": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "883d5fa099984e6da1a89e8be5e5dfed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "849cd499a84d4172b6297a294443100a": {"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_15aadb726f1c4ad4963b21b3679b7677", "IPY_MODEL_14246467416243c5a3f852bafe00119d", "IPY_MODEL_d729b6e58d7945fe9b29062e1eeb0799", "IPY_MODEL_d60b733329864ab5aa68836755fe7b35"], "layout": "IPY_MODEL_103e599465d345a29d7bec01fc98bd8e"}}, "15aadb726f1c4ad4963b21b3679b7677": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c2a78ba9f4a4e2e9db5ee945716a1b4", "style": "IPY_MODEL_b563813c0a2349dc80a056d5c0143d49", "value": "<h4 id=\"BoostingOverfit_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Boosting Overfit</b></h4>"}}, "9c2a78ba9f4a4e2e9db5ee945716a1b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b563813c0a2349dc80a056d5c0143d49": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "14246467416243c5a3f852bafe00119d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ae5dd5932c574bc2a1369b88e6d5dc02", "style": "IPY_MODEL_9a7d78ca5f4543289d52a39617026054", "value": "<p>Check for overfit caused by using too many iterations in a gradient boosted model. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_boosting_overfit.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "ae5dd5932c574bc2a1369b88e6d5dc02": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a7d78ca5f4543289d52a39617026054": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d729b6e58d7945fe9b29062e1eeb0799": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_63786d031d9746cfb47a107f783b03dc", "style": "IPY_MODEL_1b87e8ac955740deb5c0425dc55b39d2", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_fa302_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa302_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa302_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa302_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fa302_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fa302_\">\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_fa302_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fa302_row0_col1\" class=\"data row0 col1\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_fa302_row0_col2\" class=\"data row0 col2\" >Found score decline of 0%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "63786d031d9746cfb47a107f783b03dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b87e8ac955740deb5c0425dc55b39d2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d60b733329864ab5aa68836755fe7b35": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_50ccd81ba61f45d4a98ed0cf9b5ccc82", "IPY_MODEL_50ec8bb48c31431396e045f5f66080fb", "IPY_MODEL_819feb191c3c42529ad1c2e106f29e33", "IPY_MODEL_d327917ea96b4ea0938a920f8738cc1e"], "layout": "IPY_MODEL_535b310145c3497388e91bc7ce164976"}}, "50ccd81ba61f45d4a98ed0cf9b5ccc82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f8d941a6de14859bca086dce6fdec0e", "style": "IPY_MODEL_eb0b785dbd9a46e4bcd183b50110b487", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8f8d941a6de14859bca086dce6fdec0e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eb0b785dbd9a46e4bcd183b50110b487": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "50ec8bb48c31431396e045f5f66080fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_636181b91e25494cbd84cc5e62e078cb", "style": "IPY_MODEL_276e895add6547ed978d6f2e9aebd534", "value": "<div><span>\n                The check limits the boosting model to using up to N estimators each time, and plotting the\n                Neg RMSE calculated for each subset of estimators for both the train dataset and the test dataset.\n            </span></div>"}}, "636181b91e25494cbd84cc5e62e078cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "276e895add6547ed978d6f2e9aebd534": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "819feb191c3c42529ad1c2e106f29e33": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.5.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"mode": "lines+markers", "name": "Training score", "x": [1, 2, 3, 4, 5, 12, 18, 24, 31, 37, 43, 50, 56, 62, 69, 75, 81, 88, 94, 100], "y": {"dtype": "float64", "shape": [20]}, "type": "scatter", "uid": "1cfd43e6-9d8e-4f84-a773-df5e06255760"}, {"mode": "lines+markers", "name": "Test score", "x": [1, 2, 3, 4, 5, 12, 18, 24, 31, 37, 43, 50, 56, 62, 69, 75, 81, 88, 94, 100], "y": {"dtype": "float64", "shape": [20]}, "type": "scatter", "uid": "0cd666cd-b731-4bf3-aa0f-819ea1863119"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 500, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], 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"#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Neg RMSE score compared to number of boosting iteration"}, "xaxis": {"title": {"text": "Number of boosting iterations"}}, "yaxis": {"title": {"text": "Neg RMSE"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, 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"IPY_MODEL_c1796b8d148048f78cdbb65e81f93953", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "e5db6a5bbb9e4156aa732460b5093131": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c1796b8d148048f78cdbb65e81f93953": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "535b310145c3497388e91bc7ce164976": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "103e599465d345a29d7bec01fc98bd8e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d7bb82005a89488c929e6e71623f3481": {"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_ee2454e7e7ee4244b5d843ef247c41a0", "IPY_MODEL_815cec253da54c368eb476aef4398253", "IPY_MODEL_7a8a27bb74be4d6d9b0d1effbff08c3d", "IPY_MODEL_49ea8c9175934afea344a0bca06bb8c5"], "layout": "IPY_MODEL_ea26bae4afd740a2bbca452b47a04a76"}}, "ee2454e7e7ee4244b5d843ef247c41a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dc2e95059b8c42b3864fb62d863b8991", "style": "IPY_MODEL_cf179b506f244a58bd8d12b048ca49ae", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "dc2e95059b8c42b3864fb62d863b8991": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf179b506f244a58bd8d12b048ca49ae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "815cec253da54c368eb476aef4398253": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a01772ad280c4a318d3cf0eb4dca1f27", "style": "IPY_MODEL_06c8ea47119447b9887b80ba96b6b5ac", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a01772ad280c4a318d3cf0eb4dca1f27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "06c8ea47119447b9887b80ba96b6b5ac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a8a27bb74be4d6d9b0d1effbff08c3d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fcfd6eb058ed428fa8244d2747db7e40", "style": "IPY_MODEL_26134eb51fcb43debb971ef3aa49986d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_78dbb_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_78dbb_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_78dbb_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_78dbb_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_78dbb_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_78dbb_\">\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_78dbb_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_78dbb_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_78dbb_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 2.68e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "fcfd6eb058ed428fa8244d2747db7e40": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26134eb51fcb43debb971ef3aa49986d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "49ea8c9175934afea344a0bca06bb8c5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5ad69aee47174adfac5599612b30ff2a", "IPY_MODEL_732760776ccf4fa7ac78d9c26057134b", "IPY_MODEL_7d9d58c711a34bb0b6740fad8c22c196", "IPY_MODEL_f9b1017a0b454df4b8f199a3a2062cb5"], "layout": "IPY_MODEL_d1fd4fbd99364a84891594faf70ba445"}}, "5ad69aee47174adfac5599612b30ff2a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b7aa02bdd82a429c94173b92ea811a0e", "style": "IPY_MODEL_acb46b0a75b846ed84d2dc6e3b36a9d5", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b7aa02bdd82a429c94173b92ea811a0e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "acb46b0a75b846ed84d2dc6e3b36a9d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "732760776ccf4fa7ac78d9c26057134b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a9c0c4283f08452f979bee1f6925e7f0", "style": "IPY_MODEL_53cbf5f5023642f28dfa9647fe9cc031", "value": "<div>Average model inference time for one sample (in seconds): 2.68e-06</div>"}}, "a9c0c4283f08452f979bee1f6925e7f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "53cbf5f5023642f28dfa9647fe9cc031": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d9d58c711a34bb0b6740fad8c22c196": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4513f2886d804fb48bf7e446965d9963", "style": "IPY_MODEL_5eb959513eeb4533969c3843433ddd93", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Data is sampled from the original dataset, running on 1000 samples out of 1279. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "4513f2886d804fb48bf7e446965d9963": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5eb959513eeb4533969c3843433ddd93": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f9b1017a0b454df4b8f199a3a2062cb5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6af8c2cbc177445495b4eadfe92ebc04", "style": "IPY_MODEL_a0a1cf48873b4154bc9442f022e01987", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "6af8c2cbc177445495b4eadfe92ebc04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a0a1cf48873b4154bc9442f022e01987": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d1fd4fbd99364a84891594faf70ba445": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea26bae4afd740a2bbca452b47a04a76": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "62fbca60b5d844da9087edb38ffaf653": {"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_91ac32256b384ed4bbdef710fb80dc28", "IPY_MODEL_05cf5f142b9940818a030877cbf981be", "IPY_MODEL_7821d30a4310493cbeb352fd0c4f3941", "IPY_MODEL_a68fffb4ec56464e8381444cfabeeb97"], "layout": "IPY_MODEL_074a018b7ddb437a9cbbb5e67302668a"}}, "91ac32256b384ed4bbdef710fb80dc28": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87c62e5a55e94243802cde7f05bce267", "style": "IPY_MODEL_bc5f501e553141759a4e007c0bef174b", "value": "<h4 id=\"ModelInferenceTime-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Model Inference Time - Test Dataset</b></h4>"}}, "87c62e5a55e94243802cde7f05bce267": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc5f501e553141759a4e007c0bef174b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "05cf5f142b9940818a030877cbf981be": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6cafe35b34fc4fe2a0143b2e6f8002d3", "style": "IPY_MODEL_3136a44679da4986919ea6af04316b9e", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.8/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6cafe35b34fc4fe2a0143b2e6f8002d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3136a44679da4986919ea6af04316b9e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7821d30a4310493cbeb352fd0c4f3941": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d29d5c5f4d344c69bb55058bebae271a", "style": "IPY_MODEL_49cb5f16d2564678bfa0f71244624348", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_72025_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72025_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72025_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72025_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_72025_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_72025_\">\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_72025_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_72025_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_72025_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 5.04e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d29d5c5f4d344c69bb55058bebae271a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "49cb5f16d2564678bfa0f71244624348": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a68fffb4ec56464e8381444cfabeeb97": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b89283c0246d4c1aa864a85d2dcd131b", "IPY_MODEL_e2eab126454044f7988c18be7fb6f143", "IPY_MODEL_5fb9ab728acd460eb4832795bba62fa9"], "layout": "IPY_MODEL_9705902062074735a15c9453a3cbffd2"}}, "b89283c0246d4c1aa864a85d2dcd131b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0c2db045222a4c699e94382057699729", "style": "IPY_MODEL_3a62c16164a04e9eaa4f8747b2075e05", "value": "<h5><b>Additional Outputs</b></h5>"}}, "0c2db045222a4c699e94382057699729": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a62c16164a04e9eaa4f8747b2075e05": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2eab126454044f7988c18be7fb6f143": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dabc07fe82554087ab5989f3aacff500", "style": "IPY_MODEL_45351d9f5fc040a491221cee5942bbd9", "value": "<div>Average model inference time for one sample (in seconds): 5.04e-06</div>"}}, "dabc07fe82554087ab5989f3aacff500": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45351d9f5fc040a491221cee5942bbd9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5fb9ab728acd460eb4832795bba62fa9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3db6218d586f4a349b302c3f0ec8a6d2", "style": "IPY_MODEL_2540411f821c4d3a96b95907e7432dd4", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "3db6218d586f4a349b302c3f0ec8a6d2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2540411f821c4d3a96b95907e7432dd4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9705902062074735a15c9453a3cbffd2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, 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pre-wrap;\n}\n</style>\n<table id=\"T_b8630_\">\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_b8630_row0_col0\" class=\"data row0 col0\" >Roc Report - Train Dataset</td>\n      <td id=\"T_b8630_row0_col1\" class=\"data row0 col1\" >ModelValidationError: Check is relevant for models of type ['multiclass', 'binary'], but received model of type 'regression'</td>\n    </tr>\n    <tr>\n      <td id=\"T_b8630_row1_col0\" class=\"data row1 col0\" >Roc Report - Test Dataset</td>\n      <td id=\"T_b8630_row1_col1\" class=\"data row1 col1\" >ModelValidationError: Check is relevant for models of type ['multiclass', 'binary'], but received model of type 'regression'</td>\n    </tr>\n    <tr>\n      <td id=\"T_b8630_row2_col0\" class=\"data row2 col0\" >Confusion Matrix Report - Train Dataset</td>\n      <td id=\"T_b8630_row2_col1\" class=\"data row2 col1\" >ModelValidationError: Check is relevant for models of type ['multiclass', 'binary'], but received model of type 'regression'</td>\n    </tr>\n    <tr>\n      <td id=\"T_b8630_row3_col0\" class=\"data row3 col0\" >Confusion Matrix Report - Test Dataset</td>\n      <td id=\"T_b8630_row3_col1\" class=\"data row3 col1\" >ModelValidationError: Check is relevant for models of type ['multiclass', 'binary'], but received model of type 'regression'</td>\n    </tr>\n    <tr>\n      <td id=\"T_b8630_row4_col0\" class=\"data row4 col0\" >Calibration Score - Train Dataset</td>\n      <td id=\"T_b8630_row4_col1\" class=\"data row4 col1\" >ModelValidationError: Check is relevant for models of type ['multiclass', 'binary'], but received model of type 'regression'</td>\n    </tr>\n    <tr>\n      <td id=\"T_b8630_row5_col0\" class=\"data row5 col0\" >Calibration Score - Test Dataset</td>\n      <td id=\"T_b8630_row5_col1\" class=\"data row5 col1\" >ModelValidationError: Check is relevant for models of type ['multiclass', 'binary'], but received model of type 'regression'</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "224d87b0d319484da5055d62dfc7795a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ed7c2e0be434d70b735fd7926142626": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da7d6f34f5964622a67c3848174613cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72bcbbc8a6064164b546a19dbed9fcee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d6009cd0d7f2480980a306c1548b6e22": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edf0974b5fbb4bbfbd58cb87a40580d7": {"model_name": "LayoutModel", "model_module": 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.. GENERATED FROM PYTHON SOURCE LINES 94-121

Analyzing the results
--------------------------

The result showcase a number of interesting insights, first let's inspect the "Didn't Pass" section.

* :doc:`/checks_gallery/tabular/model_evaluation/plot_train_test_performance`
  check result implies that the model overfitted the training data.
* :doc:`/checks_gallery/tabular/model_evaluation/plot_regression_systematic_error`
  (test set) check result demonstrate the model small positive bias.
* :doc:`/checks_gallery/tabular/model_evaluation/plot_weak_segments_performance`
  (test set) check result visualize some specific sub-spaces on which the
  model performs poorly. Examples for those sub-spaces are
  wines with low total sulfur dioxide and wines with high alcohol percentage.

Next, let's examine the "Passed" section.

* :doc:`/checks_gallery/tabular/model_evaluation/plot_simple_model_comparison` check result states that the model
  performs better than naive baseline models, an opposite result could indicate a problem with the model
  or the data it was trained on.
* :doc:`/checks_gallery/tabular/model_evaluation/plot_boosting_overfit` check
  and the :doc:`/checks_gallery/tabular/model_evaluation/plot_unused_features` check results implies that the
  model has a well calibrating boosting stopping rule and that it make good use on the different data features.

Let's try and fix the overfitting issue found in the model.

Fix the Model and Re-run a Single Check
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 121-131

.. code-block:: default


    from deepchecks.tabular.checks import TrainTestPerformance

    gbr = GradientBoostingRegressor(n_estimators=20)
    gbr.fit(X_train, y_train)
    # Initialize the check and add an optional condition
    check = TrainTestPerformance().add_condition_train_test_relative_degradation_less_than(0.3)
    result = check.run(train_ds, test_ds, gbr)
    result.show()






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    </script>
    <script type="application/vnd.jupyter.widget-view+json">
    {"version_major": 2, "version_minor": 0, "model_id": "e33a20ef1e5e4201b783ffbcd594a4c4"}
    </script>

    </body>
    </html>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 132-143

We mitigated the overfitting to some extent. Additional model tuning is required to overcome
other issues discussed above. For now, we will update and remove the relevant conditions from the suite.

Updating an Existing Suite
--------------------------

To create our own suite, we can start with an empty suite and add checks and condition to it
(see :doc:`/user-guide/general/customizations/examples/plot_create_a_custom_suite`), or we can start with
one of the default suites and update it as demonstrated in this section.

let's inspect our model evaluation suite's structure

.. GENERATED FROM PYTHON SOURCE LINES 144-146

.. code-block:: default

    evaluation_suite





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

 .. code-block:: none


    Model Evaluation Suite: [
    	0: TrainTestPerformance
    		Conditions:
    			0: Train-Test scores relative degradation is less than 0.1
    	1: RocReport(excluded_classes=[])
    		Conditions:
    			0: AUC score for all the classes is greater than 0.7
    	2: ConfusionMatrixReport
    	3: TrainTestPredictionDrift
    		Conditions:
    			0: categorical drift score < 0.15 and numerical drift score < 0.075
    	4: SimpleModelComparison
    		Conditions:
    			0: Model performance gain over simple model is greater than 10%
    	5: WeakSegmentsPerformance(n_to_show=5)
    		Conditions:
    			0: The relative performance of weakest segment is greater than 80% of average model performance.
    	6: CalibrationScore
    	7: RegressionSystematicError
    		Conditions:
    			0: Bias ratio is less than 0.01
    	8: RegressionErrorDistribution
    		Conditions:
    			0: Kurtosis value is greater than -0.1
    	9: UnusedFeatures
    		Conditions:
    			0: Number of high variance unused features is less or equal to 5
    	10: BoostingOverfit
    		Conditions:
    			0: Test score over iterations is less than 5% from the best score
    	11: ModelInferenceTime
    		Conditions:
    			0: Average model inference time for one sample is less than 0.001
    ]



.. GENERATED FROM PYTHON SOURCE LINES 147-148

Next, we will update the Train Test Performance condition and remove the Regression Systematic Error check:

.. GENERATED FROM PYTHON SOURCE LINES 149-154

.. code-block:: default


    evaluation_suite[0].clean_conditions()
    evaluation_suite[0].add_condition_train_test_relative_degradation_less_than(0.3)
    evaluation_suite = evaluation_suite.remove(7)








.. GENERATED FROM PYTHON SOURCE LINES 155-156

Re-run the suite using:

.. GENERATED FROM PYTHON SOURCE LINES 157-161

.. code-block:: default


    result = evaluation_suite.run(train_ds, test_ds, gbr)
    result.passed(fail_if_warning=False)





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

 .. code-block:: none


    Model Evaluation Suite:
    |           | 0/11 [Time: 00:00]
    Model Evaluation Suite:
    |#          | 1/11 [Time: 00:00, Check=Train Test Performance]
    Model Evaluation Suite:
    |#####      | 5/11 [Time: 00:00, Check=Simple Model Comparison]
    Model Evaluation Suite:
    |########   | 8/11 [Time: 00:03, Check=Regression Error Distribution]
    Model Evaluation Suite:
    |########## | 10/11 [Time: 00:03, Check=Boosting Overfit]            
                                                                                 
    True



.. GENERATED FROM PYTHON SOURCE LINES 162-164

For more info about working with conditions, see the detailed
:doc:`/user-guide/general/customizations/examples/plot_configure_check_conditions` guide.


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

   **Total running time of the script:** ( 0 minutes  10.572 seconds)


.. _sphx_glr_download_user-guide_tabular_auto_quickstarts_plot_quick_model_evaluation.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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