.. 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

    <div class="output_subarea output_html rendered_html output_result">
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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:
    |           | 0/11 [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: 00:04, Check=Boosting Overfit]            
                                                                                 

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    {"version_major": 2, "version_minor": 0, "state": {"9bf984b1352043d9aed001bf44ce662d": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Model Evaluation Suite"}, "children": ["IPY_MODEL_85203464b06246e89ec051f6b98eb116"], "layout": "IPY_MODEL_51c5fa01243847939d1c32a78e9d102d"}}, "85203464b06246e89ec051f6b98eb116": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b4a4392cbcba4a3bab3d099c3f29026e", "IPY_MODEL_4b2f2cc486ad4cf2a1658a836d7d0f10", "IPY_MODEL_cbd4df41bfa945d9b7ae40b1761f2869", "IPY_MODEL_b3cd71e4e3b74ffeab0d1335b090335a", "IPY_MODEL_f59dcdbab30e42078a26c32ed5c4b4d5"], "layout": "IPY_MODEL_a4b1de90096d4a2cbb89fd0a2202723f"}}, "b4a4392cbcba4a3bab3d099c3f29026e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d0d4f7daec2468181c1fab01b6fca30", "style": "IPY_MODEL_9a8026bbd7834a12a001f7554c472490", "value": "\n            <h1 id=\"summary_SYLZTGXN0GUO4KH2VLPYS280X\">Model Evaluation Suite</h1>\n            <p>\n                The suite is composed of various checks such as: Model Inference Time, Roc Report, Weak Segments Performance, etc...<br>\n                Each check may contain conditions (which will result in pass / fail / warning / error\n                , represented by \n<span style=\"color: green;display:inline-block\">\u2713</span> /\n<span style=\"color: red;display:inline-block\">\u2716</span> /\n<span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> /\n<span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>\n) as well as other outputs such as plots or tables.<br>\n                Suites, checks and conditions can all be modified. Read more about\n                <a href=https://docs.deepchecks.com/en/stable/examples/guides/create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n            </p>\n\n"}}, "8d0d4f7daec2468181c1fab01b6fca30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a8026bbd7834a12a001f7554c472490": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4b2f2cc486ad4cf2a1658a836d7d0f10": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a15eb13df32a4e46a4b534ef9f0e1a53", "IPY_MODEL_32acfd6171974e4dba8a8e30fe12680c"], "layout": "IPY_MODEL_1397d86e85cb41a5aac1ebbb6032addc"}}, "a15eb13df32a4e46a4b534ef9f0e1a53": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6df463b99a64b519c575234b98fb4c4", "style": "IPY_MODEL_4d845a3409ee44c29f4432a55ec24a81", "value": "<span id=\"summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"></span>"}}, "d6df463b99a64b519c575234b98fb4c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d845a3409ee44c29f4432a55ec24a81": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "32acfd6171974e4dba8a8e30fe12680c": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Pass"}, "children": ["IPY_MODEL_140d9d3d4d3f4152b3c93c3c8ef4292f"], "layout": "IPY_MODEL_c83ee81334bd4dd9b723e6315e856254", "selected_index": null}}, "140d9d3d4d3f4152b3c93c3c8ef4292f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_28c66b0379f84f8ea7ddec6a2b16b56c", "IPY_MODEL_a0664eb7bd944d51b45157225cfd2f58", "IPY_MODEL_e046253400694742b3219949fd0cb16a", "IPY_MODEL_6be5237c9cec4a4fa814dac06ccf8153", "IPY_MODEL_bfd0d55aab974cdea3eba98c4f4c081e", "IPY_MODEL_6be5237c9cec4a4fa814dac06ccf8153", "IPY_MODEL_12acfb641f3645c69b8d99625be67c8c", "IPY_MODEL_6be5237c9cec4a4fa814dac06ccf8153", "IPY_MODEL_2097a9d68a684632870006587b386afe"], "layout": "IPY_MODEL_f82624c027974c628c87b52300f55022"}}, "28c66b0379f84f8ea7ddec6a2b16b56c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c4d45064627e49a1979a35d21b248c47", "style": "IPY_MODEL_863098c94fff478e8ee8e722471b008f", "value": "<style type=\"text/css\">\n#T_b98e4_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b98e4_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b98e4_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b98e4_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b98e4_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b98e4_\">\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_b98e4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b98e4_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestPerformance_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Train Test Performance</a></td>\n      <td id=\"T_b98e4_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_b98e4_row0_col3\" class=\"data row0 col3\" >3 scores failed. Found max degradation of 27.89% for metric R2</td>\n    </tr>\n    <tr>\n      <td id=\"T_b98e4_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b98e4_row1_col1\" class=\"data row1 col1\" ><a href=\"#RegressionErrorDistribution-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Regression Error Distribution - Test Dataset</a></td>\n      <td id=\"T_b98e4_row1_col2\" class=\"data row1 col2\" >Systematic error ratio lower than 0.01</td>\n      <td id=\"T_b98e4_row1_col3\" class=\"data row1 col3\" >Found systematic error to rmse ratio of 0.05</td>\n    </tr>\n    <tr>\n      <td id=\"T_b98e4_row2_col0\" class=\"data row2 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_b98e4_row2_col1\" class=\"data row2 col1\" ><a href=\"#WeakSegmentsPerformance-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Weak Segments Performance - Train Dataset</a></td>\n      <td id=\"T_b98e4_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_b98e4_row2_col3\" class=\"data row2 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_b98e4_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_b98e4_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Weak Segments Performance - Test Dataset</a></td>\n      <td id=\"T_b98e4_row3_col2\" class=\"data row3 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_b98e4_row3_col3\" class=\"data row3 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    <tr>\n      <td id=\"T_b98e4_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_b98e4_row4_col1\" class=\"data row4 col1\" ><a href=\"#RegressionErrorDistribution-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\">Regression Error Distribution - Test Dataset</a></td>\n      <td id=\"T_b98e4_row4_col2\" class=\"data row4 col2\" >Kurtosis value higher than -0.1</td>\n      <td id=\"T_b98e4_row4_col3\" class=\"data row4 col3\" >Found kurtosis value of 0.4632</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c4d45064627e49a1979a35d21b248c47": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "863098c94fff478e8ee8e722471b008f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a0664eb7bd944d51b45157225cfd2f58": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b89d9a8cae62423980983945742ad47a", "style": "IPY_MODEL_72bbc951e59e4047aaec370854e8df88", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "b89d9a8cae62423980983945742ad47a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72bbc951e59e4047aaec370854e8df88": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e046253400694742b3219949fd0cb16a": {"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_540e11b0f62e49a885208eee1fd3740d", "IPY_MODEL_b7348bff040c4c188c4d42453dc1a7ea", "IPY_MODEL_5d4f2ea162554e349dd20fc92864d174", "IPY_MODEL_bca5478d869c4330873cbbb389556887"], "layout": "IPY_MODEL_9a4fb830d6a14fe38cbfa3a34821d9ec"}}, "540e11b0f62e49a885208eee1fd3740d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b8bfdee5e954464d9259ada5c39a2600", "style": "IPY_MODEL_2bc89a4e51424a0794e01931133748c1", "value": "<h4 id=\"TrainTestPerformance_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Train Test Performance</b></h4>"}}, "b8bfdee5e954464d9259ada5c39a2600": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2bc89a4e51424a0794e01931133748c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7348bff040c4c188c4d42453dc1a7ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1299f9e0e87b49809a99d5b546f87bed", "style": "IPY_MODEL_6950a41dba474c039d94518d2dfa858b", "value": "<p>Summarize given model performance on the train and test datasets based on selected scorers. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1299f9e0e87b49809a99d5b546f87bed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6950a41dba474c039d94518d2dfa858b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d4f2ea162554e349dd20fc92864d174": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a1ce0a3a31443ebaed8a169f683ed99", "style": "IPY_MODEL_dd4e0fd74b5e456ab310bec3f26d7ea3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_09e13_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_09e13_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_09e13_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_09e13_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_09e13_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_09e13_\">\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_09e13_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_09e13_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_09e13_row0_col2\" class=\"data row0 col2\" >3 scores failed. Found max degradation of 27.89% for metric R2</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "8a1ce0a3a31443ebaed8a169f683ed99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dd4e0fd74b5e456ab310bec3f26d7ea3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bca5478d869c4330873cbbb389556887": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_23e162af42df4f8084706083eb509325", "IPY_MODEL_da7a37f1b9a34dbeb8624b32e7851771", "IPY_MODEL_9169348d30b6426bbd36ef0d0104ca73"], "layout": "IPY_MODEL_2d10678dd8c947f2b5cfb8c9d8929a62"}}, "23e162af42df4f8084706083eb509325": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a341294bbdc24ceeb86e7cfaf215b354", "style": "IPY_MODEL_795d16b659d04f798530a8ea382c5ebd", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a341294bbdc24ceeb86e7cfaf215b354": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "795d16b659d04f798530a8ea382c5ebd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da7a37f1b9a34dbeb8624b32e7851771": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=%{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": "a596a7dd-dfe5-4d11-906b-25d9f2226727"}, {"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": "3455af73-96b4-47e3-a123-8fe764e0a884"}, {"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": "03c6c002-1799-4261-b329-01860d94d4e0"}, {"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": "4e23f3f0-c39f-4ea5-984b-01d8f93cd34f"}, {"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": "2449dc87-e2a2-4416-88a8-6cf6e7039e91"}, {"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": "4f8ba37e-ccb5-4d5b-87ac-7d4fbb8dc040"}], "_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": ""}, 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{}}, "bfd0d55aab974cdea3eba98c4f4c081e": {"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_beb090be58e5474aa3aba6603f45d326", "IPY_MODEL_642111cc83ef4999a36d3e51b7f2b869", "IPY_MODEL_9ac5f40f921048ab904b758df0e72027", "IPY_MODEL_614a8120c9814750a91057130887bbf4"], "layout": "IPY_MODEL_634929f623a247209c08eb2e099fd3f0"}}, "beb090be58e5474aa3aba6603f45d326": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d2f7dc68574943149bec12cf7a1d5477", "style": "IPY_MODEL_16d39368887540889dee75c874c64b95", "value": "<h4 id=\"RegressionErrorDistribution-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Regression Error Distribution - Test Dataset</b></h4>"}}, "d2f7dc68574943149bec12cf7a1d5477": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16d39368887540889dee75c874c64b95": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "642111cc83ef4999a36d3e51b7f2b869": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e10f9e8abb984eb491103a4468ccd8aa", "style": "IPY_MODEL_753efd15708542ae9557590d5b8b2dd9", "value": "<p>Check for systematic error and abnormal shape in the regression error distribution. <a href=\"https://docs.deepchecks.com/0.9/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>"}}, "e10f9e8abb984eb491103a4468ccd8aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "753efd15708542ae9557590d5b8b2dd9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9ac5f40f921048ab904b758df0e72027": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90eab23bc92345f888c161e6e201bf3f", "style": "IPY_MODEL_2f72aed690b0480a8c55c810546351a8", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_94c06_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_94c06_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_94c06_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_94c06_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_94c06_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_94c06_\">\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_94c06_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_94c06_row0_col1\" class=\"data row0 col1\" >Systematic error ratio lower than 0.01</td>\n      <td id=\"T_94c06_row0_col2\" class=\"data row0 col2\" >Found systematic error to rmse ratio of 0.05</td>\n    </tr>\n    <tr>\n      <td id=\"T_94c06_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_94c06_row1_col1\" class=\"data row1 col1\" >Kurtosis value higher than -0.1</td>\n      <td id=\"T_94c06_row1_col2\" class=\"data row1 col2\" >Found kurtosis value of 0.4632</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "90eab23bc92345f888c161e6e201bf3f": {"model_name": 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{"_dom_classes": [], "layout": "IPY_MODEL_c6f97f3e80f14e2eb35ef3be7bb99151", "style": "IPY_MODEL_c694c354f04c49d2bd0f2c3f77653541", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c6f97f3e80f14e2eb35ef3be7bb99151": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c694c354f04c49d2bd0f2c3f77653541": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0cd3e73046c14eb984fac428a54b28a1": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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, 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{\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_56586_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_56586_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_56586_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_56586_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_56586_\">\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_56586_level0_row0\" class=\"row_heading level0 row0\" >1449</th>\n      <td id=\"T_56586_row0_col0\" class=\"data row0 col0\" >7.20</td>\n      <td id=\"T_56586_row0_col1\" class=\"data row0 col1\" >0.38</td>\n      <td id=\"T_56586_row0_col2\" class=\"data row0 col2\" >0.31</td>\n      <td id=\"T_56586_row0_col3\" class=\"data row0 col3\" >2.00</td>\n      <td id=\"T_56586_row0_col4\" class=\"data row0 col4\" >0.06</td>\n      <td id=\"T_56586_row0_col5\" class=\"data row0 col5\" >15.00</td>\n      <td id=\"T_56586_row0_col6\" class=\"data row0 col6\" >29.00</td>\n      <td id=\"T_56586_row0_col7\" class=\"data row0 col7\" >0.99</td>\n      <td id=\"T_56586_row0_col8\" class=\"data row0 col8\" >3.23</td>\n      <td id=\"T_56586_row0_col9\" class=\"data row0 col9\" >0.76</td>\n      <td id=\"T_56586_row0_col10\" class=\"data row0 col10\" >11.30</td>\n      <td id=\"T_56586_row0_col11\" class=\"data row0 col11\" >8</td>\n      <td id=\"T_56586_row0_col12\" class=\"data row0 col12\" >6.28</td>\n      <td id=\"T_56586_row0_col13\" class=\"data row0 col13\" >1.72</td>\n    </tr>\n    <tr>\n      <th id=\"T_56586_level0_row1\" class=\"row_heading level0 row1\" >1436</th>\n      <td id=\"T_56586_row1_col0\" class=\"data row1 col0\" >10.00</td>\n      <td id=\"T_56586_row1_col1\" class=\"data row1 col1\" >0.38</td>\n      <td id=\"T_56586_row1_col2\" class=\"data row1 col2\" >0.38</td>\n      <td id=\"T_56586_row1_col3\" class=\"data row1 col3\" >1.60</td>\n      <td id=\"T_56586_row1_col4\" class=\"data row1 col4\" >0.17</td>\n      <td id=\"T_56586_row1_col5\" class=\"data row1 col5\" >27.00</td>\n      <td id=\"T_56586_row1_col6\" class=\"data row1 col6\" >90.00</td>\n      <td id=\"T_56586_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_56586_row1_col8\" class=\"data row1 col8\" >3.15</td>\n      <td id=\"T_56586_row1_col9\" class=\"data row1 col9\" >0.65</td>\n      <td id=\"T_56586_row1_col10\" class=\"data row1 col10\" >8.50</td>\n      <td id=\"T_56586_row1_col11\" class=\"data row1 col11\" >5</td>\n      <td id=\"T_56586_row1_col12\" class=\"data row1 col12\" >3.52</td>\n      <td id=\"T_56586_row1_col13\" class=\"data row1 col13\" >1.48</td>\n    </tr>\n    <tr>\n      <th id=\"T_56586_level0_row2\" class=\"row_heading level0 row2\" >588</th>\n      <td id=\"T_56586_row2_col0\" class=\"data row2 col0\" >5.00</td>\n      <td id=\"T_56586_row2_col1\" class=\"data row2 col1\" >0.42</td>\n      <td id=\"T_56586_row2_col2\" class=\"data row2 col2\" >0.24</td>\n      <td id=\"T_56586_row2_col3\" class=\"data row2 col3\" >2.00</td>\n      <td id=\"T_56586_row2_col4\" class=\"data row2 col4\" >0.06</td>\n      <td id=\"T_56586_row2_col5\" class=\"data row2 col5\" >19.00</td>\n      <td id=\"T_56586_row2_col6\" class=\"data row2 col6\" >50.00</td>\n      <td id=\"T_56586_row2_col7\" class=\"data row2 col7\" >0.99</td>\n      <td id=\"T_56586_row2_col8\" class=\"data row2 col8\" >3.72</td>\n      <td id=\"T_56586_row2_col9\" class=\"data row2 col9\" >0.74</td>\n      <td id=\"T_56586_row2_col10\" class=\"data row2 col10\" >14.00</td>\n      <td id=\"T_56586_row2_col11\" class=\"data row2 col11\" >8</td>\n      <td id=\"T_56586_row2_col12\" class=\"data row2 col12\" >6.53</td>\n      <td id=\"T_56586_row2_col13\" class=\"data row2 col13\" >1.47</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "df6ff3bc136e47119d6469db331c15e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc85057211a04c238ee3fa00448ffff7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ca430738933f4031bbe481d49e914eeb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_447a5c2868774b18b86bd0420230a110", "style": "IPY_MODEL_3ee47624d39e4ba7a2f0aa0c99c16a09", "value": "<div>Largest under estimation errors:</div>"}}, "447a5c2868774b18b86bd0420230a110": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ee47624d39e4ba7a2f0aa0c99c16a09": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12564cc196fd4f82bbe3c7ffe610116c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bd28fc15f5bc410ebff28840f97d6d57", "style": "IPY_MODEL_931ef354051845519c91c9c76f72d5fd", "value": "<style type=\"text/css\">\n#T_29336_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_29336_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_29336_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_29336_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_29336_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_29336_\">\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_29336_level0_row0\" class=\"row_heading level0 row0\" >1505</th>\n      <td id=\"T_29336_row0_col0\" class=\"data row0 col0\" >6.70</td>\n      <td id=\"T_29336_row0_col1\" class=\"data row0 col1\" >0.76</td>\n      <td id=\"T_29336_row0_col2\" class=\"data row0 col2\" >0.02</td>\n      <td id=\"T_29336_row0_col3\" class=\"data row0 col3\" >1.80</td>\n      <td id=\"T_29336_row0_col4\" class=\"data row0 col4\" >0.08</td>\n      <td id=\"T_29336_row0_col5\" class=\"data row0 col5\" >6.00</td>\n      <td id=\"T_29336_row0_col6\" class=\"data row0 col6\" >12.00</td>\n      <td id=\"T_29336_row0_col7\" class=\"data row0 col7\" >1.00</td>\n      <td id=\"T_29336_row0_col8\" class=\"data row0 col8\" >3.55</td>\n      <td id=\"T_29336_row0_col9\" class=\"data row0 col9\" >0.63</td>\n      <td id=\"T_29336_row0_col10\" class=\"data row0 col10\" >9.95</td>\n      <td id=\"T_29336_row0_col11\" class=\"data row0 col11\" >3</td>\n      <td id=\"T_29336_row0_col12\" class=\"data row0 col12\" >5.46</td>\n      <td id=\"T_29336_row0_col13\" class=\"data row0 col13\" >-2.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_29336_level0_row1\" class=\"row_heading level0 row1\" >462</th>\n      <td id=\"T_29336_row1_col0\" class=\"data row1 col0\" >11.00</td>\n      <td id=\"T_29336_row1_col1\" class=\"data row1 col1\" >0.26</td>\n      <td id=\"T_29336_row1_col2\" class=\"data row1 col2\" >0.68</td>\n      <td id=\"T_29336_row1_col3\" class=\"data row1 col3\" >2.55</td>\n      <td id=\"T_29336_row1_col4\" class=\"data row1 col4\" >0.09</td>\n      <td id=\"T_29336_row1_col5\" class=\"data row1 col5\" >10.00</td>\n      <td id=\"T_29336_row1_col6\" class=\"data row1 col6\" >25.00</td>\n      <td id=\"T_29336_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_29336_row1_col8\" class=\"data row1 col8\" >3.18</td>\n      <td id=\"T_29336_row1_col9\" class=\"data row1 col9\" >0.61</td>\n      <td id=\"T_29336_row1_col10\" class=\"data row1 col10\" >11.80</td>\n      <td id=\"T_29336_row1_col11\" class=\"data row1 col11\" >5</td>\n      <td id=\"T_29336_row1_col12\" class=\"data row1 col12\" >6.76</td>\n      <td id=\"T_29336_row1_col13\" class=\"data row1 col13\" >-1.76</td>\n    </tr>\n    <tr>\n      <th id=\"T_29336_level0_row2\" class=\"row_heading level0 row2\" >1484</th>\n      <td id=\"T_29336_row2_col0\" class=\"data row2 col0\" >6.80</td>\n      <td id=\"T_29336_row2_col1\" class=\"data row2 col1\" >0.91</td>\n      <td id=\"T_29336_row2_col2\" class=\"data row2 col2\" >0.06</td>\n      <td id=\"T_29336_row2_col3\" class=\"data row2 col3\" >2.00</td>\n      <td id=\"T_29336_row2_col4\" class=\"data row2 col4\" >0.06</td>\n      <td id=\"T_29336_row2_col5\" class=\"data row2 col5\" >4.00</td>\n      <td id=\"T_29336_row2_col6\" class=\"data row2 col6\" >11.00</td>\n      <td id=\"T_29336_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_29336_row2_col8\" class=\"data row2 col8\" >3.53</td>\n      <td id=\"T_29336_row2_col9\" class=\"data row2 col9\" >0.64</td>\n      <td id=\"T_29336_row2_col10\" class=\"data row2 col10\" >10.90</td>\n      <td id=\"T_29336_row2_col11\" class=\"data row2 col11\" >4</td>\n      <td id=\"T_29336_row2_col12\" class=\"data row2 col12\" >5.62</td>\n      <td id=\"T_29336_row2_col13\" class=\"data row2 col13\" >-1.62</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bd28fc15f5bc410ebff28840f97d6d57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "931ef354051845519c91c9c76f72d5fd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1ffb6445453a406e8962f236d2906227": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1fd5f9e717ca4915aa7ee0c8bfbd7bfe", "style": "IPY_MODEL_60b2ba0765c3437496c7ae40f1d71813", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\" style=\"font-size: 14px;\">Go to top</a>"}}, "1fd5f9e717ca4915aa7ee0c8bfbd7bfe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "60b2ba0765c3437496c7ae40f1d71813": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8c9e62e9272e4dd5997e3174330f152a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "634929f623a247209c08eb2e099fd3f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "12acfb641f3645c69b8d99625be67c8c": {"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_bb1ead4577b943eca6631b976aeebaf0", "IPY_MODEL_290c656edd8b4865b52648a2856f7383", "IPY_MODEL_91ef70b6d5fc4dcc930bacd0f43d6f55", "IPY_MODEL_b9eb3c88085b4144bf7ef590088b03bc"], "layout": "IPY_MODEL_601a9486f7e945b2a52f979e59797d5a"}}, "bb1ead4577b943eca6631b976aeebaf0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bb00229a68514a95aae5d12e607030cd", "style": "IPY_MODEL_76f21bce210040a7bc847fb2ab08e794", "value": "<h4 id=\"WeakSegmentsPerformance-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Weak Segments Performance - Train Dataset</b></h4>"}}, "bb00229a68514a95aae5d12e607030cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "76f21bce210040a7bc847fb2ab08e794": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "290c656edd8b4865b52648a2856f7383": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1360e8a5948d41c7bba63b84adb55776", "style": "IPY_MODEL_e2d44a9697cf4168b08e94e6af6f8a5a", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.9/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>"}}, "1360e8a5948d41c7bba63b84adb55776": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e2d44a9697cf4168b08e94e6af6f8a5a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "91ef70b6d5fc4dcc930bacd0f43d6f55": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bead5f33cd4e44ce9c53be9c90baaf97", "style": "IPY_MODEL_7dcf56a7ae964abcbae7defed6ada50d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_9b9be_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9b9be_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9b9be_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9b9be_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_9b9be_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_9b9be_\">\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_9b9be_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_9b9be_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_9b9be_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"}}, "bead5f33cd4e44ce9c53be9c90baaf97": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7dcf56a7ae964abcbae7defed6ada50d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b9eb3c88085b4144bf7ef590088b03bc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2a0b2c74331a46f7a00bba062d28f631", "IPY_MODEL_9e10ad5b535146368be1fb501be4d0fb", "IPY_MODEL_cfe61d934b0146c096ba372a98949e9e", "IPY_MODEL_a106d9f731974fada41eb071e2c71595"], "layout": "IPY_MODEL_88e7f2ac2e74473f9a9941be306d2a3a"}}, "2a0b2c74331a46f7a00bba062d28f631": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a37aaeaf9a84ad8aca6a73924bdacc8", "style": "IPY_MODEL_4c1e8fd7226a4eaaa072fb5627741294", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4a37aaeaf9a84ad8aca6a73924bdacc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c1e8fd7226a4eaaa072fb5627741294": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e10ad5b535146368be1fb501be4d0fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cf6477a57fb84898820fe11cffd8a656", "style": "IPY_MODEL_382684236a35476e9c743d0e05c10f43", "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>"}}, "cf6477a57fb84898820fe11cffd8a656": {"model_name": "LayoutModel", 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"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 (percent of data) volatile acidity vs total sulfur dioxide"}, "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], "title": {"text": "total sulfur dioxide"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "8a6fa38c7a1949d2bab4ce1b296afbbe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_915007ffeb8f4f1d89deb154b6fa4162", "style": "IPY_MODEL_c4e8f5cca4424b1997ee63ad7db75df7", "value": "<div>Check ran on 1279 data samples. Average Neg RMSE score is -0.5.</div>"}}, "915007ffeb8f4f1d89deb154b6fa4162": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c4e8f5cca4424b1997ee63ad7db75df7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ca443c5fbc824899aaff80945caa2217": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3c56393ccac4ebb8605747bf4a90160": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6795edf7f0c34cd09ac57c13af50f0c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a106d9f731974fada41eb071e2c71595": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6a27600b3164f10b8de0a5996fade53", "style": "IPY_MODEL_4e1c392e95e844238aed6a52bdf63f07", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\" style=\"font-size: 14px;\">Go to top</a>"}}, "d6a27600b3164f10b8de0a5996fade53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4e1c392e95e844238aed6a52bdf63f07": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88e7f2ac2e74473f9a9941be306d2a3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "601a9486f7e945b2a52f979e59797d5a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2097a9d68a684632870006587b386afe": {"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_72c22146ae744560ae64f7cb040dfcf7", "IPY_MODEL_7bd519e96cf04ee1a2b1a065bb9de6db", "IPY_MODEL_5de33dee70574c10a6841a0c8390c985", "IPY_MODEL_13f9e3fb5b364082a44f9ca1cc1e6ad6"], "layout": "IPY_MODEL_73eb6ed51b7a4305a29c041b7ec24ab9"}}, "72c22146ae744560ae64f7cb040dfcf7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_758cd4d7dad7441a8fa4b5d175bb752e", "style": "IPY_MODEL_5b40913b56e7457a92979d72857e0b95", "value": "<h4 id=\"WeakSegmentsPerformance-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\"><b>Weak Segments Performance - Test Dataset</b></h4>"}}, "758cd4d7dad7441a8fa4b5d175bb752e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5b40913b56e7457a92979d72857e0b95": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7bd519e96cf04ee1a2b1a065bb9de6db": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4239b93c7a51467bb0784743e2513784", "style": "IPY_MODEL_0aafe5a911cc40708e630de09d81351d", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.9/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>"}}, "4239b93c7a51467bb0784743e2513784": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0aafe5a911cc40708e630de09d81351d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5de33dee70574c10a6841a0c8390c985": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e2e4ce310f5e4cfcbaa899daf64f408c", "style": "IPY_MODEL_b7e6d75b0a0f40f69d41e4677528900b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_47124_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47124_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47124_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47124_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_47124_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_47124_\">\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_47124_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_47124_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_47124_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"}}, "e2e4ce310f5e4cfcbaa899daf64f408c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7e6d75b0a0f40f69d41e4677528900b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "13f9e3fb5b364082a44f9ca1cc1e6ad6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_788d2f40a6324fd0865a50f9a9f41a0a", "IPY_MODEL_ae33aa77eaac484f9064c2de94feea62", "IPY_MODEL_e87a4c4624064440ae1c0f5a4a7bef05", "IPY_MODEL_0b8a7a0472b6451a880d1ef1385286c6"], "layout": "IPY_MODEL_2ab6e683e4d743469d2d80e0ba47c5ba"}}, "788d2f40a6324fd0865a50f9a9f41a0a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8df116964d2c402f95bd32e46360ddbc", "style": "IPY_MODEL_d976ee77041544e888829b6f6d44d5cb", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8df116964d2c402f95bd32e46360ddbc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d976ee77041544e888829b6f6d44d5cb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ae33aa77eaac484f9064c2de94feea62": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_09a23442b0804881a4036e6cd1be2611", "style": "IPY_MODEL_51b11d47211e41c6966d08b9efbfc01a", "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>"}}, "09a23442b0804881a4036e6cd1be2611": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51b11d47211e41c6966d08b9efbfc01a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e87a4c4624064440ae1c0f5a4a7bef05": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": 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"scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "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}}, "1869dcc680114ce0a0af04f4de1247e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51894a9025e4462dbd59b5bdd1d66938", "style": "IPY_MODEL_982af1a704194911b5849efa5612a8a5", "value": "<div>Check ran on 320 data samples. Average Neg RMSE score is -0.6.</div>"}}, "51894a9025e4462dbd59b5bdd1d66938": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "982af1a704194911b5849efa5612a8a5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8925d3a3787549e99e27d68801f459b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90715efdb94840a2a2641a94ef7c4416": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b9033a74db884690ab5514330f2199d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0b8a7a0472b6451a880d1ef1385286c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_642b57f0643d4e99a73fddfe08d56fb1", "style": "IPY_MODEL_b22d1435ee944741a8d25a2650c9dc31", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-KXWD8\" style=\"font-size: 14px;\">Go to top</a>"}}, "642b57f0643d4e99a73fddfe08d56fb1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b22d1435ee944741a8d25a2650c9dc31": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2ab6e683e4d743469d2d80e0ba47c5ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73eb6ed51b7a4305a29c041b7ec24ab9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f82624c027974c628c87b52300f55022": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c83ee81334bd4dd9b723e6315e856254": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1397d86e85cb41a5aac1ebbb6032addc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cbd4df41bfa945d9b7ae40b1761f2869": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f218c870c6e5440398f330115f1d4e5a", "IPY_MODEL_c7f4423610ff4b9a8a6b272c8b05152f"], "layout": "IPY_MODEL_b690af7e51e140ddad4b007bbbe6f3ac"}}, "f218c870c6e5440398f330115f1d4e5a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8fdbc19feace413a8318153ae6a05f9a", "style": "IPY_MODEL_c1f37a789ceb40a59c0753ae219f145c", "value": "<span id=\"summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"></span>"}}, "8fdbc19feace413a8318153ae6a05f9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c1f37a789ceb40a59c0753ae219f145c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c7f4423610ff4b9a8a6b272c8b05152f": {"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_8b029724f6b4456cae9aeaf4752c359a"], "layout": "IPY_MODEL_8be182a58c54498f9ebcdad312353249", "selected_index": null}}, "8b029724f6b4456cae9aeaf4752c359a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e4f8d6085d2340b4bac3e971e0b67c4c", "IPY_MODEL_5ae9c95dad69401d8befa0a4c0064f77", "IPY_MODEL_121bf32c361f443ca8e1149c739f0d2a", "IPY_MODEL_053b70e11c33473d87d6bae622112ffb", "IPY_MODEL_0a977d6d254b45c69d13b70c9790ec8c", "IPY_MODEL_053b70e11c33473d87d6bae622112ffb", "IPY_MODEL_cfd1a8f50c4d4e9bb016586c053ee499", "IPY_MODEL_053b70e11c33473d87d6bae622112ffb", "IPY_MODEL_7210e1d243df403abbb873571ed3b4ab", "IPY_MODEL_053b70e11c33473d87d6bae622112ffb", "IPY_MODEL_b5125bcc29d440bd8360de4b0f8d642f", "IPY_MODEL_053b70e11c33473d87d6bae622112ffb", "IPY_MODEL_cd0638ac426441eb8bf1ffdd5abcb45f"], "layout": "IPY_MODEL_9939508f177c4c54b5c8a1d253d04216"}}, "e4f8d6085d2340b4bac3e971e0b67c4c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e3a1dc7160f46a18f359cd471252682", "style": "IPY_MODEL_07c15c365ab24890803812362a57ecf0", "value": "<style type=\"text/css\">\n#T_c2ec3_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c2ec3_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c2ec3_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c2ec3_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c2ec3_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c2ec3_\">\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_c2ec3_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row0_col1\" class=\"data row0 col1\" ><a href=\"#TrainTestPredictionDrift_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Train Test Prediction Drift</a></td>\n      <td id=\"T_c2ec3_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_c2ec3_row0_col3\" class=\"data row0 col3\" >Found model prediction Earth Mover's Distance drift score of 0.02</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row1_col1\" class=\"data row1 col1\" ><a href=\"#SimpleModelComparison_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Simple Model Comparison</a></td>\n      <td id=\"T_c2ec3_row1_col2\" class=\"data row1 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_c2ec3_row1_col3\" class=\"data row1 col3\" >All metrics passed, metric's gain: {'Neg RMSE': '25.72%'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row2_col1\" class=\"data row2 col1\" ><a href=\"#RegressionErrorDistribution-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Regression Error Distribution - Train Dataset</a></td>\n      <td id=\"T_c2ec3_row2_col2\" class=\"data row2 col2\" >Kurtosis value higher than -0.1</td>\n      <td id=\"T_c2ec3_row2_col3\" class=\"data row2 col3\" >Found kurtosis value of 0.55157</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row3_col1\" class=\"data row3 col1\" ><a href=\"#RegressionErrorDistribution-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Regression Error Distribution - Train Dataset</a></td>\n      <td id=\"T_c2ec3_row3_col2\" class=\"data row3 col2\" >Systematic error ratio lower than 0.01</td>\n      <td id=\"T_c2ec3_row3_col3\" class=\"data row3 col3\" >Found systematic error to rmse ratio of 5.70E-16</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row4_col0\" class=\"data row4 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row4_col1\" class=\"data row4 col1\" ><a href=\"#BoostingOverfit_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Boosting Overfit</a></td>\n      <td id=\"T_c2ec3_row4_col2\" class=\"data row4 col2\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_c2ec3_row4_col3\" class=\"data row4 col3\" >Found score decline of 0%</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row5_col0\" class=\"data row5 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row5_col1\" class=\"data row5 col1\" ><a href=\"#ModelInferenceTime-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Model Inference Time - Train Dataset</a></td>\n      <td id=\"T_c2ec3_row5_col2\" class=\"data row5 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_c2ec3_row5_col3\" class=\"data row5 col3\" >Found average inference time (seconds): 2.73e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row6_col0\" class=\"data row6 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row6_col1\" class=\"data row6 col1\" ><a href=\"#ModelInferenceTime-TestDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\">Model Inference Time - Test Dataset</a></td>\n      <td id=\"T_c2ec3_row6_col2\" class=\"data row6 col2\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_c2ec3_row6_col3\" class=\"data row6 col3\" >Found average inference time (seconds): 5.15e-06</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row7_col0\" class=\"data row7 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row7_col1\" class=\"data row7 col1\" >Unused Features - Train Dataset</td>\n      <td id=\"T_c2ec3_row7_col2\" class=\"data row7 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_c2ec3_row7_col3\" class=\"data row7 col3\" >Found 0 high variance unused features</td>\n    </tr>\n    <tr>\n      <td id=\"T_c2ec3_row8_col0\" class=\"data row8 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c2ec3_row8_col1\" class=\"data row8 col1\" >Unused Features - Test Dataset</td>\n      <td id=\"T_c2ec3_row8_col2\" class=\"data row8 col2\" >Number of high variance unused features is less or equal to 5</td>\n      <td id=\"T_c2ec3_row8_col3\" class=\"data row8 col3\" >Found 0 high variance unused features</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4e3a1dc7160f46a18f359cd471252682": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07c15c365ab24890803812362a57ecf0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5ae9c95dad69401d8befa0a4c0064f77": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0ee7f9a95ad047fdb34e19c190f44ec4", "style": "IPY_MODEL_1b9727d02a1748d699ddecffd3e8f5f9", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "0ee7f9a95ad047fdb34e19c190f44ec4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b9727d02a1748d699ddecffd3e8f5f9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "121bf32c361f443ca8e1149c739f0d2a": {"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_0ff614e01a804b27b4070a400dd44a7b", "IPY_MODEL_99c8aa1e4586446788eb78afccd3c722", "IPY_MODEL_be1b8d8206a24bc888fffcf998b2aebc", "IPY_MODEL_fcb49d18dc8443158081e7a4464ff349"], "layout": "IPY_MODEL_caa51dec046641ce98df72a6653f365c"}}, "0ff614e01a804b27b4070a400dd44a7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f542dd2b80f0491e82aeb2927fe71ab1", "style": "IPY_MODEL_ef9a163fcaf94dfbad5590bf8dba479e", "value": "<h4 id=\"TrainTestPredictionDrift_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Train Test Prediction Drift</b></h4>"}}, "f542dd2b80f0491e82aeb2927fe71ab1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef9a163fcaf94dfbad5590bf8dba479e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "99c8aa1e4586446788eb78afccd3c722": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_696ec15b5fc8452088ad2379cae45f7d", "style": "IPY_MODEL_f8201ff2be8741efbb639164232b422c", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_train_test_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "696ec15b5fc8452088ad2379cae45f7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f8201ff2be8741efbb639164232b422c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be1b8d8206a24bc888fffcf998b2aebc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9fa5439ba3ef47a481c9239cf695115e", "style": "IPY_MODEL_62930667229c4fb6a5dff7b0cab5be83", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d2f29_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f29_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f29_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f29_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d2f29_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d2f29_\">\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_d2f29_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d2f29_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.075</td>\n      <td id=\"T_d2f29_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"}}, "9fa5439ba3ef47a481c9239cf695115e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "62930667229c4fb6a5dff7b0cab5be83": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fcb49d18dc8443158081e7a4464ff349": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9cae704e8fc94673b1b542081f61e8bf", "IPY_MODEL_1194febfe35f445fbaf47b9dd74fb688", "IPY_MODEL_4e629f166c9f4c1fb39d75cc3c6adb63", "IPY_MODEL_91dc643441c84512bc72ce91ac421eaa", "IPY_MODEL_c5a4147ae6d642b3a9927ae129ebe5b8"], "layout": "IPY_MODEL_c7d16a55a38643419cd8f8ae4817f1cd"}}, "9cae704e8fc94673b1b542081f61e8bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_39937a28ff4143b7a308526717ab6b43", "style": "IPY_MODEL_b4bf993cc6894f12a30bd37b17396940", "value": "<h5><b>Additional Outputs</b></h5>"}}, "39937a28ff4143b7a308526717ab6b43": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4bf993cc6894f12a30bd37b17396940": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1194febfe35f445fbaf47b9dd74fb688": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7080acf7ec06413c8b05c2a4ab09c10a", "style": "IPY_MODEL_61883d163c32460b82ffc537dd1331bb", "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>"}}, "7080acf7ec06413c8b05c2a4ab09c10a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61883d163c32460b82ffc537dd1331bb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4e629f166c9f4c1fb39d75cc3c6adb63": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_85c277bf86d4480584c00251928a620d", "style": "IPY_MODEL_2924f5627aa34c7f9f92f0c0ef21e893", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "85c277bf86d4480584c00251928a620d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2924f5627aa34c7f9f92f0c0ef21e893": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "91dc643441c84512bc72ce91ac421eaa": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.018195974788310455], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "a0afad1e-a819-4157-a09e-7b8087b3e428"}, {"fill": "tozeroy", "line": 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"model_module_version": "1.2.0", "state": {}}, "053b70e11c33473d87d6bae622112ffb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cedf6743b6984a3ba568d769390c5948", "style": "IPY_MODEL_f1895fc2f9414351b85e2ccca518aae1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "cedf6743b6984a3ba568d769390c5948": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1895fc2f9414351b85e2ccca518aae1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0a977d6d254b45c69d13b70c9790ec8c": {"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_e5b355f4018c46228c95f1c5550e7154", "IPY_MODEL_0313215357654f18a7744159f5af79f9", "IPY_MODEL_9d028dade6b64ecc9fc23f282f3db4d8", "IPY_MODEL_30bc09e1824146178547b230d4bfb25a"], "layout": "IPY_MODEL_c7d08ea3cf9b40a1b6f094eb5ca12d88"}}, "e5b355f4018c46228c95f1c5550e7154": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0b2dab5d3a834d49af22c27d33a11f91", "style": "IPY_MODEL_e14bb3696f9248f0a289a39fbeae35c1", "value": "<h4 id=\"SimpleModelComparison_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Simple Model Comparison</b></h4>"}}, "0b2dab5d3a834d49af22c27d33a11f91": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e14bb3696f9248f0a289a39fbeae35c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0313215357654f18a7744159f5af79f9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd37843d4e5f4a5cba6a38db057044e5", "style": "IPY_MODEL_79452a29495a45eea823f03b235e3e65", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "fd37843d4e5f4a5cba6a38db057044e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "79452a29495a45eea823f03b235e3e65": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9d028dade6b64ecc9fc23f282f3db4d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ef9e89e02a6244c6aa93157d9318ff9d", "style": "IPY_MODEL_45f57499a3b64c3c955d30ef5cf769c0", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d658b_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d658b_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d658b_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d658b_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d658b_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d658b_\">\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_d658b_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d658b_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_d658b_row0_col2\" class=\"data row0 col2\" >All metrics passed, metric's gain: {'Neg RMSE': '25.72%'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ef9e89e02a6244c6aa93157d9318ff9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45f57499a3b64c3c955d30ef5cf769c0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "30bc09e1824146178547b230d4bfb25a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_99f7f07724cc47639ed9dc4e4f6e094e", "IPY_MODEL_9b94ce88a9d74123b7f0934a3d124c8d", "IPY_MODEL_db75185798a0443bb8ba0ccac4a36eae"], "layout": "IPY_MODEL_5e5abd7f48ea4f6ca3427ef2a2d98d37"}}, "99f7f07724cc47639ed9dc4e4f6e094e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ed9238728a21426dbe6291fd328f0f48", "style": "IPY_MODEL_7a1e0e6938374dd4b6221fed0e141bb1", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ed9238728a21426dbe6291fd328f0f48": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a1e0e6938374dd4b6221fed0e141bb1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9b94ce88a9d74123b7f0934a3d124c8d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=%{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": "4a42f045-9d62-49a4-b721-a72a88b7a2bc"}, {"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": "bf7f733e-d4ed-40af-a978-d3aae031bac7"}], "_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": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "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": "1YVDR/BE478="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "mP6w3eDw6b8="}]}, "db75185798a0443bb8ba0ccac4a36eae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c120b5c65cc4eceab42b7e7cf7222ce", "style": "IPY_MODEL_73dfe5a223d044c7899171f98214a108", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "5c120b5c65cc4eceab42b7e7cf7222ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73dfe5a223d044c7899171f98214a108": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5e5abd7f48ea4f6ca3427ef2a2d98d37": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7d08ea3cf9b40a1b6f094eb5ca12d88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cfd1a8f50c4d4e9bb016586c053ee499": {"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_0a7571986f1745138e67e2ead2de7cb7", "IPY_MODEL_bfce1875872e4aafbd382a974a5ab768", "IPY_MODEL_2a51e00a4aab4afa8dbdf8c1b17c8827", "IPY_MODEL_112ac3d56ed24a4a8478b71da6a90330"], "layout": "IPY_MODEL_3bfa5ab5050544e290a1afdf75294feb"}}, "0a7571986f1745138e67e2ead2de7cb7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2d5307809a7b4bdbbfd8983f9435e814", "style": "IPY_MODEL_f0b7b2b19a3d4040a3319e3fe52dca58", "value": "<h4 id=\"RegressionErrorDistribution-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Regression Error Distribution - Train Dataset</b></h4>"}}, "2d5307809a7b4bdbbfd8983f9435e814": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f0b7b2b19a3d4040a3319e3fe52dca58": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bfce1875872e4aafbd382a974a5ab768": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cd25124d4a0a4c18a384e77358bd9ea5", "style": "IPY_MODEL_fa811163f025406e83c82a6bcef3e8e4", "value": "<p>Check for systematic error and abnormal shape in the regression error distribution. <a href=\"https://docs.deepchecks.com/0.9/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>"}}, "cd25124d4a0a4c18a384e77358bd9ea5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa811163f025406e83c82a6bcef3e8e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2a51e00a4aab4afa8dbdf8c1b17c8827": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e85260a465fb4c7293d887883e4f95f3", "style": "IPY_MODEL_b65ecbdf8c5147ba8ac138bd35a64d65", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_63cb3_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63cb3_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63cb3_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63cb3_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_63cb3_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_63cb3_\">\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_63cb3_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_63cb3_row0_col1\" class=\"data row0 col1\" >Kurtosis value higher than -0.1</td>\n      <td id=\"T_63cb3_row0_col2\" class=\"data row0 col2\" >Found kurtosis value of 0.55157</td>\n    </tr>\n    <tr>\n      <td id=\"T_63cb3_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_63cb3_row1_col1\" class=\"data row1 col1\" >Systematic error ratio lower than 0.01</td>\n      <td id=\"T_63cb3_row1_col2\" class=\"data row1 col2\" >Found systematic error to rmse ratio of 5.70E-16</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "e85260a465fb4c7293d887883e4f95f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b65ecbdf8c5147ba8ac138bd35a64d65": {"model_name": 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Outputs</b></h5>"}}, "f4de879abdd64c30b570dc59f59a6145": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d227698320eb46ef9a7f3093631a1a39": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "39b19d923cb746c1a394728b7b91b1b7": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": [1279]}, "xaxis": "x", "yaxis": "y", "type": "histogram", "uid": "3a24f5de-71b2-4fce-8aff-337769a2bf53"}], "_dom_classes": [], 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"}]}, "3b1a62a4b7d3432ea4991fccf224cda9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e7b8f485e34748038b7946893b4b9fc3", "style": "IPY_MODEL_787a5df5b6714c12a301cf43cd256f29", "value": "<div>Largest over estimation errors:</div>"}}, "e7b8f485e34748038b7946893b4b9fc3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "787a5df5b6714c12a301cf43cd256f29": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6ccd10558e5246f68abd309e33a30045": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_addcb29a91324c7dbdf8dfdc02636aae", "style": "IPY_MODEL_17929b5e161849edb0c80b34d75e8ecf", "value": "<style type=\"text/css\">\n#T_42946_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42946_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42946_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42946_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_42946_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_42946_\">\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_42946_level0_row0\" class=\"row_heading level0 row0\" >7</th>\n      <td id=\"T_42946_row0_col0\" class=\"data row0 col0\" >7.30</td>\n      <td id=\"T_42946_row0_col1\" class=\"data row0 col1\" >0.65</td>\n      <td id=\"T_42946_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_42946_row0_col3\" class=\"data row0 col3\" >1.20</td>\n      <td id=\"T_42946_row0_col4\" class=\"data row0 col4\" >0.07</td>\n      <td id=\"T_42946_row0_col5\" class=\"data row0 col5\" >15.00</td>\n      <td id=\"T_42946_row0_col6\" class=\"data row0 col6\" >21.00</td>\n      <td id=\"T_42946_row0_col7\" class=\"data row0 col7\" >0.99</td>\n      <td id=\"T_42946_row0_col8\" class=\"data row0 col8\" >3.39</td>\n      <td id=\"T_42946_row0_col9\" class=\"data row0 col9\" >0.47</td>\n      <td id=\"T_42946_row0_col10\" class=\"data row0 col10\" >10.00</td>\n      <td id=\"T_42946_row0_col11\" class=\"data row0 col11\" >7</td>\n      <td id=\"T_42946_row0_col12\" class=\"data row0 col12\" >5.37</td>\n      <td id=\"T_42946_row0_col13\" class=\"data row0 col13\" >1.63</td>\n    </tr>\n    <tr>\n      <th id=\"T_42946_level0_row1\" class=\"row_heading level0 row1\" >8</th>\n      <td id=\"T_42946_row1_col0\" class=\"data row1 col0\" >7.80</td>\n      <td id=\"T_42946_row1_col1\" class=\"data row1 col1\" >0.58</td>\n      <td id=\"T_42946_row1_col2\" class=\"data row1 col2\" >0.02</td>\n      <td id=\"T_42946_row1_col3\" class=\"data row1 col3\" >2.00</td>\n      <td id=\"T_42946_row1_col4\" class=\"data row1 col4\" >0.07</td>\n      <td id=\"T_42946_row1_col5\" class=\"data row1 col5\" >9.00</td>\n      <td id=\"T_42946_row1_col6\" class=\"data row1 col6\" >18.00</td>\n      <td id=\"T_42946_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_42946_row1_col8\" class=\"data row1 col8\" >3.36</td>\n      <td id=\"T_42946_row1_col9\" class=\"data row1 col9\" >0.57</td>\n      <td id=\"T_42946_row1_col10\" class=\"data row1 col10\" >9.50</td>\n      <td id=\"T_42946_row1_col11\" class=\"data row1 col11\" >7</td>\n      <td id=\"T_42946_row1_col12\" class=\"data row1 col12\" >5.44</td>\n      <td id=\"T_42946_row1_col13\" class=\"data row1 col13\" >1.56</td>\n    </tr>\n    <tr>\n      <th id=\"T_42946_level0_row2\" class=\"row_heading level0 row2\" >1403</th>\n      <td id=\"T_42946_row2_col0\" class=\"data row2 col0\" >7.20</td>\n      <td id=\"T_42946_row2_col1\" class=\"data row2 col1\" >0.33</td>\n      <td id=\"T_42946_row2_col2\" class=\"data row2 col2\" >0.33</td>\n      <td id=\"T_42946_row2_col3\" class=\"data row2 col3\" >1.70</td>\n      <td id=\"T_42946_row2_col4\" class=\"data row2 col4\" >0.06</td>\n      <td id=\"T_42946_row2_col5\" class=\"data row2 col5\" >3.00</td>\n      <td id=\"T_42946_row2_col6\" class=\"data row2 col6\" >13.00</td>\n      <td id=\"T_42946_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_42946_row2_col8\" class=\"data row2 col8\" >3.23</td>\n      <td id=\"T_42946_row2_col9\" class=\"data row2 col9\" >1.10</td>\n      <td id=\"T_42946_row2_col10\" class=\"data row2 col10\" >10.00</td>\n      <td id=\"T_42946_row2_col11\" class=\"data row2 col11\" >8</td>\n      <td id=\"T_42946_row2_col12\" class=\"data row2 col12\" >6.44</td>\n      <td id=\"T_42946_row2_col13\" class=\"data row2 col13\" >1.56</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "addcb29a91324c7dbdf8dfdc02636aae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "17929b5e161849edb0c80b34d75e8ecf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db5278aeec544926b02713722d9b735f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e48d9d18cb454970ac5f998e8320a7dc", "style": "IPY_MODEL_92e2a234199a4d0086480441683ff4f5", "value": "<div>Largest under estimation errors:</div>"}}, "e48d9d18cb454970ac5f998e8320a7dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "92e2a234199a4d0086480441683ff4f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ccd5da00d7004bc6af9364e3f966727f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2928efcd6be74de2b2df22427a329189", "style": "IPY_MODEL_d382914fc69e41cb81c5c29c1c838123", "value": "<style type=\"text/css\">\n#T_60917_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60917_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60917_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60917_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60917_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_60917_\">\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_60917_level0_row0\" class=\"row_heading level0 row0\" >1469</th>\n      <td id=\"T_60917_row0_col0\" class=\"data row0 col0\" >7.30</td>\n      <td id=\"T_60917_row0_col1\" class=\"data row0 col1\" >0.98</td>\n      <td id=\"T_60917_row0_col2\" class=\"data row0 col2\" >0.05</td>\n      <td id=\"T_60917_row0_col3\" class=\"data row0 col3\" >2.10</td>\n      <td id=\"T_60917_row0_col4\" class=\"data row0 col4\" >0.06</td>\n      <td id=\"T_60917_row0_col5\" class=\"data row0 col5\" >20.00</td>\n      <td id=\"T_60917_row0_col6\" class=\"data row0 col6\" >49.00</td>\n      <td id=\"T_60917_row0_col7\" class=\"data row0 col7\" >1.00</td>\n      <td id=\"T_60917_row0_col8\" class=\"data row0 col8\" >3.31</td>\n      <td id=\"T_60917_row0_col9\" class=\"data row0 col9\" >0.55</td>\n      <td id=\"T_60917_row0_col10\" class=\"data row0 col10\" >9.70</td>\n      <td id=\"T_60917_row0_col11\" class=\"data row0 col11\" >3</td>\n      <td id=\"T_60917_row0_col12\" class=\"data row0 col12\" >5.02</td>\n      <td id=\"T_60917_row0_col13\" class=\"data row0 col13\" >-2.02</td>\n    </tr>\n    <tr>\n      <th id=\"T_60917_level0_row1\" class=\"row_heading level0 row1\" >1478</th>\n      <td id=\"T_60917_row1_col0\" class=\"data row1 col0\" >7.10</td>\n      <td id=\"T_60917_row1_col1\" class=\"data row1 col1\" >0.88</td>\n      <td id=\"T_60917_row1_col2\" class=\"data row1 col2\" >0.05</td>\n      <td id=\"T_60917_row1_col3\" class=\"data row1 col3\" >5.70</td>\n      <td id=\"T_60917_row1_col4\" class=\"data row1 col4\" >0.08</td>\n      <td id=\"T_60917_row1_col5\" class=\"data row1 col5\" >3.00</td>\n      <td id=\"T_60917_row1_col6\" class=\"data row1 col6\" >14.00</td>\n      <td id=\"T_60917_row1_col7\" class=\"data row1 col7\" >1.00</td>\n      <td id=\"T_60917_row1_col8\" class=\"data row1 col8\" >3.40</td>\n      <td id=\"T_60917_row1_col9\" class=\"data row1 col9\" >0.52</td>\n      <td id=\"T_60917_row1_col10\" class=\"data row1 col10\" >10.20</td>\n      <td id=\"T_60917_row1_col11\" class=\"data row1 col11\" >3</td>\n      <td id=\"T_60917_row1_col12\" class=\"data row1 col12\" >4.58</td>\n      <td id=\"T_60917_row1_col13\" class=\"data row1 col13\" >-1.58</td>\n    </tr>\n    <tr>\n      <th id=\"T_60917_level0_row2\" class=\"row_heading level0 row2\" >1423</th>\n      <td id=\"T_60917_row2_col0\" class=\"data row2 col0\" >6.40</td>\n      <td id=\"T_60917_row2_col1\" class=\"data row2 col1\" >0.53</td>\n      <td id=\"T_60917_row2_col2\" class=\"data row2 col2\" >0.09</td>\n      <td id=\"T_60917_row2_col3\" class=\"data row2 col3\" >3.90</td>\n      <td id=\"T_60917_row2_col4\" class=\"data row2 col4\" >0.12</td>\n      <td id=\"T_60917_row2_col5\" class=\"data row2 col5\" >14.00</td>\n      <td id=\"T_60917_row2_col6\" class=\"data row2 col6\" >31.00</td>\n      <td id=\"T_60917_row2_col7\" class=\"data row2 col7\" >1.00</td>\n      <td id=\"T_60917_row2_col8\" class=\"data row2 col8\" >3.50</td>\n      <td id=\"T_60917_row2_col9\" class=\"data row2 col9\" >0.67</td>\n      <td id=\"T_60917_row2_col10\" class=\"data row2 col10\" >11.00</td>\n      <td id=\"T_60917_row2_col11\" class=\"data row2 col11\" >4</td>\n      <td id=\"T_60917_row2_col12\" class=\"data row2 col12\" >5.57</td>\n      <td id=\"T_60917_row2_col13\" class=\"data row2 col13\" >-1.57</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2928efcd6be74de2b2df22427a329189": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d382914fc69e41cb81c5c29c1c838123": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59d7001129d543c38739484db98116fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e73e3ff4162e4e59949186d1649b2157", "style": "IPY_MODEL_faf7630c9da0407faaaa38dd214991ab", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "e73e3ff4162e4e59949186d1649b2157": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "faf7630c9da0407faaaa38dd214991ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fb764642f8764f3f9c11969aefa08df3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bfa5ab5050544e290a1afdf75294feb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7210e1d243df403abbb873571ed3b4ab": {"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_4970c59013ba4249a6bf87e1df874931", "IPY_MODEL_cbf321ae2c5d4cfb9b9169ce089fb961", "IPY_MODEL_92eb946c893a4a1fb87c610271018d57", "IPY_MODEL_c905f402c4e64f82b1f4ba62723f43f9"], "layout": "IPY_MODEL_dee11f35a8954d00b67a0bc59022b8a6"}}, "4970c59013ba4249a6bf87e1df874931": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f952578b4f1847a99fd1e8365e16dbf3", "style": "IPY_MODEL_3d65cb6962ee4a68903ced36d31642bf", "value": "<h4 id=\"BoostingOverfit_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Boosting Overfit</b></h4>"}}, "f952578b4f1847a99fd1e8365e16dbf3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d65cb6962ee4a68903ced36d31642bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbf321ae2c5d4cfb9b9169ce089fb961": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_065d577dbbf044609cd42053a9e73b17", "style": "IPY_MODEL_878e74dd5a7943d1a6ee3f41cbd0e2ee", "value": "<p>Check for overfit caused by using too many iterations in a gradient boosted model. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_boosting_overfit.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "065d577dbbf044609cd42053a9e73b17": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "878e74dd5a7943d1a6ee3f41cbd0e2ee": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "92eb946c893a4a1fb87c610271018d57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db3707873f764cf3a6a6d25797841f04", "style": "IPY_MODEL_834161e96ec44508a4b93896ac2179aa", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_64880_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64880_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64880_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64880_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64880_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_64880_\">\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_64880_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_64880_row0_col1\" class=\"data row0 col1\" >Test score over iterations is less than 5% from the best score</td>\n      <td id=\"T_64880_row0_col2\" class=\"data row0 col2\" >Found score decline of 0%</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "db3707873f764cf3a6a6d25797841f04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "834161e96ec44508a4b93896ac2179aa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c905f402c4e64f82b1f4ba62723f43f9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6a0c961aee9c4161b45a68192ac67ac3", "IPY_MODEL_db5df5bd7ae3465a9e63c3b0af82529b", "IPY_MODEL_b30fdc5b4bd648148e9fd8c73d4c0325", "IPY_MODEL_1fd7f38a201442218719fbaafafe16f8"], "layout": "IPY_MODEL_460c3f690dd44ae0bff328758c305a0f"}}, "6a0c961aee9c4161b45a68192ac67ac3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dce2e9aea6014a7c893aa22b79bca19c", "style": "IPY_MODEL_7ebddfd7b8744aa1a0b2edda7d786c09", "value": "<h5><b>Additional Outputs</b></h5>"}}, "dce2e9aea6014a7c893aa22b79bca19c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ebddfd7b8744aa1a0b2edda7d786c09": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db5df5bd7ae3465a9e63c3b0af82529b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4eefc9f86ec248c38282442b556deb72", "style": "IPY_MODEL_42fe6ab52db244bf8ecf0fc2cc67c0f3", "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>"}}, "4eefc9f86ec248c38282442b556deb72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42fe6ab52db244bf8ecf0fc2cc67c0f3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b30fdc5b4bd648148e9fd8c73d4c0325": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.11.0", "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": "c6fa62f4-3e61-42db-bfb5-d5b6bbb46a3c"}, {"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": "aa6f7408-54b4-4965-8dea-3eded4a7d684"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, 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"#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "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": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "WDULWKbz6L+bHa6/jDrov1iM2RJKkue/VKzmKIMG57+941u9mYbmv1dgEJ7QVeS/NdzHmZ5V47/HHqcdpLXiv6vQwlwHNOK/7xs6a+fm4b92iU9zjqDhvyZQfW9jXOG/VNhO1Gcp4b/9kus7Wvvgv5xZydwF0OC/6GXUPtim4L9PrJQ9uWrgv7QwdsIoMuC/JUIhBesJ4L+pqUlHG+/fvw=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "unlEePAs6b83bo2wYIjov3aNyVcE6+e/sWiAdsFm578vISMhPPbmvw+Z9uBxBOW/gFePQJxZ5L9mV8h53PrjvyeQSuuKouO/ZNJCHfuJ47/TU/h+wHvjv3EZmzmLc+O/sZAMJap347+UwK6zY3Hjvzn+WdwpYOO/1vQQLClm47/Zz3eXQVbjv6OHHh1mSeO/j/VMPOlO47/VhUNH8ETjvw=="}]}, "1fd7f38a201442218719fbaafafe16f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_602347ad82cb4fbca642166b029006e5", "style": "IPY_MODEL_d125a9667ff84661a898efab91da28c2", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "602347ad82cb4fbca642166b029006e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d125a9667ff84661a898efab91da28c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "460c3f690dd44ae0bff328758c305a0f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dee11f35a8954d00b67a0bc59022b8a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5125bcc29d440bd8360de4b0f8d642f": {"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_833289eaa7644b3d921c82fd5b71eea4", "IPY_MODEL_51b5c8ddf639405f969075cb63c51055", "IPY_MODEL_03203253b332474fadcfcccb1c7800b7", "IPY_MODEL_bc6eb8d0ed5a46f5a861dc12681539a6"], "layout": "IPY_MODEL_de515948525e48f984855dded5c974cd"}}, "833289eaa7644b3d921c82fd5b71eea4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f3ec646f373461baa28eaf53f3a658c", "style": "IPY_MODEL_c2bccd450d9b477a9dc0f1393aa4963b", "value": "<h4 id=\"ModelInferenceTime-TrainDataset_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\"><b>Model Inference Time - Train Dataset</b></h4>"}}, "3f3ec646f373461baa28eaf53f3a658c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2bccd450d9b477a9dc0f1393aa4963b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "51b5c8ddf639405f969075cb63c51055": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_abeefaa7bef9492ebe9f4c9ae9a6349b", "style": "IPY_MODEL_3eaf9430684944b0969455534b2d0a4e", "value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "abeefaa7bef9492ebe9f4c9ae9a6349b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3eaf9430684944b0969455534b2d0a4e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "03203253b332474fadcfcccb1c7800b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b3254266d8dc4c9e9657c4d338424e55", "style": "IPY_MODEL_555e66ca4ef8426ba2112607f54972a9", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_19ad5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19ad5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19ad5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19ad5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_19ad5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_19ad5_\">\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_19ad5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_19ad5_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_19ad5_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 2.73e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b3254266d8dc4c9e9657c4d338424e55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "555e66ca4ef8426ba2112607f54972a9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bc6eb8d0ed5a46f5a861dc12681539a6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3cddc7c754684215a330250313e68117", "IPY_MODEL_4574c62862d44b12907350750d695c0e", "IPY_MODEL_e92cc983ea4f426783d0ae23d84deedb", "IPY_MODEL_154144cfa00e493fbc330b9543918434"], "layout": "IPY_MODEL_66706a90048d4f6580e4816fec2f7212"}}, "3cddc7c754684215a330250313e68117": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07a091415157478d944f9082ecf1e517", "style": "IPY_MODEL_ab101164b72643bab3983fa59a3c6419", "value": "<h5><b>Additional Outputs</b></h5>"}}, "07a091415157478d944f9082ecf1e517": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab101164b72643bab3983fa59a3c6419": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4574c62862d44b12907350750d695c0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f371b0167524c3ca7e4765ae8a8efd4", "style": "IPY_MODEL_6e8435050c8d4d76b000203d8c046777", "value": "<div>Average model inference time for one sample (in seconds): 2.73e-06</div>"}}, "8f371b0167524c3ca7e4765ae8a8efd4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e8435050c8d4d76b000203d8c046777": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e92cc983ea4f426783d0ae23d84deedb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e010ff69df84a24be4eeb74b864dd71", "style": "IPY_MODEL_7c4d940ffe79438794ec53f0d8e29222", "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>"}}, "3e010ff69df84a24be4eeb74b864dd71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7c4d940ffe79438794ec53f0d8e29222": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "154144cfa00e493fbc330b9543918434": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5fc892cd5c7f481ba5525b6f39248c00", "style": "IPY_MODEL_aaa55de3ae2d457db5874facdf774b2b", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "5fc892cd5c7f481ba5525b6f39248c00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aaa55de3ae2d457db5874facdf774b2b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "66706a90048d4f6580e4816fec2f7212": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de515948525e48f984855dded5c974cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd0638ac426441eb8bf1ffdd5abcb45f": {"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_9966ee8c40054dc4bf64769b4c854f40", "IPY_MODEL_a3e49b73b181461e9b78b1a77abb3cad", "IPY_MODEL_6e09a4f6882a49648e13b8b3d0075b5b", "IPY_MODEL_a5ac0c9d3ffd424cb06e4c0d26e38d69"], "layout": "IPY_MODEL_b7078ab9ce944fdd8d55e4a6113c656a"}}, "9966ee8c40054dc4bf64769b4c854f40": 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"value": "<p>Measure model average inference time (in seconds) per sample. <a href=\"https://docs.deepchecks.com/0.9/checks_gallery/tabular/model_evaluation/plot_model_inference_time.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "92e97afd772c43ad9e385c9bf4e6a0af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6cc2462287bd430b9119e611bec0efe7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6e09a4f6882a49648e13b8b3d0075b5b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_14aa078b900e4ceda97e70b4375d3cb5", "style": "IPY_MODEL_4b9b072a9fde4586add2e612671f2aab", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_acdc5_ table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acdc5_ thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acdc5_ tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acdc5_ th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_acdc5_ td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_acdc5_\">\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_acdc5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_acdc5_row0_col1\" class=\"data row0 col1\" >Average model inference time for one sample is less than 0.001</td>\n      <td id=\"T_acdc5_row0_col2\" class=\"data row0 col2\" >Found average inference time (seconds): 5.15e-06</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "14aa078b900e4ceda97e70b4375d3cb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b9b072a9fde4586add2e612671f2aab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a5ac0c9d3ffd424cb06e4c0d26e38d69": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3a7bb8142357456d9385a43dae39e87d", "IPY_MODEL_0561a00e903048b7b5ad4f4114a0ba1b", "IPY_MODEL_c54151ae148548e59c7f84eaba25bf22"], "layout": "IPY_MODEL_2cb4a4a05cb64036a7e69b191548931f"}}, "3a7bb8142357456d9385a43dae39e87d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb66afbc26a14531b00223dc4438191b", "style": "IPY_MODEL_3ab43ed216a4423eae7bbb77f3e38de1", "value": "<h5><b>Additional Outputs</b></h5>"}}, "fb66afbc26a14531b00223dc4438191b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ab43ed216a4423eae7bbb77f3e38de1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0561a00e903048b7b5ad4f4114a0ba1b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d7e8b3ffc9174fd0b482d8bee64930ea", "style": "IPY_MODEL_b255b7bbf97a46b68d6eede165d386f2", "value": "<div>Average model inference time for one sample (in seconds): 5.15e-06</div>"}}, "d7e8b3ffc9174fd0b482d8bee64930ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b255b7bbf97a46b68d6eede165d386f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c54151ae148548e59c7f84eaba25bf22": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b8708fcbc20d4eb89a4e026e664f7f52", "style": "IPY_MODEL_902e395e8ef44521931db23bfbcc324a", "value": "<br><a href=\"#summary_SYLZTGXN0GUO4KH2VLPYS280X-section-IL3FB\" style=\"font-size: 14px;\">Go to top</a>"}}, "b8708fcbc20d4eb89a4e026e664f7f52": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "902e395e8ef44521931db23bfbcc324a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2cb4a4a05cb64036a7e69b191548931f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, 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{"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7fa3f7eddda3482d8e1937016bd55556", "style": "IPY_MODEL_9ff398835b1b4850baa62a373716ad42"}}, "7fa3f7eddda3482d8e1937016bd55556": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ff398835b1b4850baa62a373716ad42": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0eefba60f84c4a6ea048cff069aca6cb": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_38f5a32cc47145168aaf9c9338d1da25"], "layout": "IPY_MODEL_0005544132fb48dcb5850bc4d57c54bc", "selected_index": null}}, "38f5a32cc47145168aaf9c9338d1da25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3736240cba514da5b334559ce85d2827", "style": "IPY_MODEL_0153a6cdc0c84506b545d07887def8f5", "value": "<p>No outputs to show.</p>"}}, "3736240cba514da5b334559ce85d2827": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0153a6cdc0c84506b545d07887def8f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0005544132fb48dcb5850bc4d57c54bc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "81b71a033b984728b8e62d374113f321": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f59dcdbab30e42078a26c32ed5c4b4d5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3e8078d615de43e595f4bac0c7324ff6", "IPY_MODEL_075177e7ecd8431487feb09895e8a9d8"], "layout": "IPY_MODEL_0aa82bc4423e4d19875a832fb792aa3a"}}, "3e8078d615de43e595f4bac0c7324ff6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_042585cecd0742a897fbef80855bf4c0", "style": "IPY_MODEL_ec511310f401431c8f7b1c029c61031d"}}, "042585cecd0742a897fbef80855bf4c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec511310f401431c8f7b1c029c61031d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "075177e7ecd8431487feb09895e8a9d8": {"model_name": "AccordionModel", "model_module": 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pre-wrap;\n}\n</style>\n<table id=\"T_9265f_\">\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_9265f_row0_col0\" class=\"data row0 col0\" >Roc Report - Train Dataset</td>\n      <td id=\"T_9265f_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for regression tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_9265f_row1_col0\" class=\"data row1 col0\" >Roc Report - Test Dataset</td>\n      <td id=\"T_9265f_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for regression tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_9265f_row2_col0\" class=\"data row2 col0\" >Confusion Matrix Report - Train Dataset</td>\n      <td id=\"T_9265f_row2_col1\" class=\"data row2 col1\" >Check is irrelevant for regression tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_9265f_row3_col0\" class=\"data row3 col0\" >Confusion Matrix Report - Test Dataset</td>\n      <td id=\"T_9265f_row3_col1\" class=\"data row3 col1\" >Check is irrelevant for regression tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_9265f_row4_col0\" class=\"data row4 col0\" >Calibration Score - Train Dataset</td>\n      <td id=\"T_9265f_row4_col1\" class=\"data row4 col1\" >Check is irrelevant for regression tasks</td>\n    </tr>\n    <tr>\n      <td id=\"T_9265f_row5_col0\" class=\"data row5 col0\" >Calibration Score - Test Dataset</td>\n      <td id=\"T_9265f_row5_col1\" class=\"data row5 col1\" >Check is irrelevant for regression tasks</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5f176ae075ed4906baf2908dbe26c083": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38b759da8cc041be90c1f247582c6a5c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96e56eb3706c4f28aac790474ace85c3": {"model_name": 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    <br />
    <br />

.. 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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"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": {}, 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    </script>
    <script type="application/vnd.jupyter.widget-view+json">
    {"version_major": 2, "version_minor": 0, "model_id": "0185d05a609441dcbb1abbc923786631"}
    </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(drift_mode=auto)
    		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: RegressionErrorDistribution
    		Conditions:
    			0: Kurtosis value higher than -0.1
    			1: Systematic error ratio lower than 0.01
    	8: UnusedFeatures
    		Conditions:
    			0: Number of high variance unused features is less or equal to 5
    	9: BoostingOverfit
    		Conditions:
    			0: Test score over iterations is less than 5% from the best score
    	10: 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:
    |          | 0/10 [Time: 00:00]
    Model Evaluation Suite:
    |#         | 1/10 [Time: 00:00, Check=Train Test Performance]
    Model Evaluation Suite:
    |#####     | 5/10 [Time: 00:00, Check=Simple Model Comparison]
    Model Evaluation Suite:
    |########  | 8/10 [Time: 00:03, Check=Unused Features]        
    Model Evaluation Suite:
    |##########| 10/10 [Time: 00:03, Check=Model Inference Time]
                                                                                    
    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  10.579 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>`_