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.. "checks_gallery/tabular/performance/plot_multi_model_performance_report.py"
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.. only:: html

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        Click :ref:`here <sphx_glr_download_checks_gallery_tabular_performance_plot_multi_model_performance_report.py>`
        to download the full example code

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

.. _sphx_glr_checks_gallery_tabular_performance_plot_multi_model_performance_report.py:


Multi Model Performance Report
******************************

.. GENERATED FROM PYTHON SOURCE LINES 8-10

Multiclass
==========

.. GENERATED FROM PYTHON SOURCE LINES 10-19

.. code-block:: default


    from sklearn.datasets import load_iris
    from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.tree import DecisionTreeClassifier

    from deepchecks.tabular import Dataset
    from deepchecks.tabular.checks.performance import MultiModelPerformanceReport








.. GENERATED FROM PYTHON SOURCE LINES 20-33

.. code-block:: default


    iris = load_iris(as_frame=True)
    train, test = train_test_split(iris.frame, test_size=0.33, random_state=42)

    train_ds = Dataset(train, label="target")
    test_ds = Dataset(test, label="target")

    features = train_ds.data[train_ds.features]
    label = train_ds.data[train_ds.label_name]
    clf1 = AdaBoostClassifier().fit(features, label)
    clf2 = RandomForestClassifier().fit(features, label)
    clf3 = DecisionTreeClassifier().fit(features, label)








.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: default


    MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

    <script
        src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"
        integrity="sha512-c3Nl8+7g4LMSTdrm621y7kf9v3SDPnhxLNhcjFJbKECVnmZHTdo+IRO05sNLTH/D3vA6u1X32ehoLC7WFVdheg=="
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    <h4><b>Multi Model Performance Report</b></h4><p>Summarize performance scores for multiple models on test datasets.</p><h5><b>Additional Outputs</b></h5><div>                            <div id="53674506-4227-4772-b6c5-bf598a8b776d" class="plotly-graph-div" style="height:525px; width:100%;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("53674506-4227-4772-b6c5-bf598a8b776d")) {                    Plotly.newPlot(                        "53674506-4227-4772-b6c5-bf598a8b776d",                        [{"alignmentgroup":"True","bingroup":"x","histfunc":"sum","hovertemplate":"Model=AdaBoostClassifier<br>Metric=F1<br>value=%{x}<br>sum of 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.. GENERATED FROM PYTHON SOURCE LINES 38-40

Regression
==========

.. GENERATED FROM PYTHON SOURCE LINES 40-45

.. code-block:: default


    from sklearn.datasets import load_diabetes
    from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor
    from sklearn.tree import DecisionTreeRegressor








.. GENERATED FROM PYTHON SOURCE LINES 46-59

.. code-block:: default


    diabetes = load_diabetes(as_frame=True)
    train, test = train_test_split(diabetes.frame, test_size=0.33, random_state=42)

    train_ds = Dataset(train, label="target", cat_features=['sex'])
    test_ds = Dataset(test, label="target", cat_features=['sex'])

    features = train_ds.data[train_ds.features]
    label = train_ds.data[train_ds.label_name]
    clf1 = AdaBoostRegressor().fit(features, label)
    clf2 = RandomForestRegressor().fit(features, label)
    clf3 = DecisionTreeRegressor().fit(features, label)








.. GENERATED FROM PYTHON SOURCE LINES 60-62

.. code-block:: default


    MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])





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    <h4><b>Multi Model Performance Report</b></h4><p>Summarize performance scores for multiple models on test datasets.</p><h5><b>Additional Outputs</b></h5><div>                            <div id="688df01b-cf30-47f2-995f-17b38a758d32" class="plotly-graph-div" style="height:525px; width:100%;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("688df01b-cf30-47f2-995f-17b38a758d32")) {                    Plotly.newPlot(                        "688df01b-cf30-47f2-995f-17b38a758d32",                        [{"alignmentgroup":"True","bingroup":"x","histfunc":"sum","hovertemplate":"Model=%{x}<br>Metric=Neg RMSE<br>sum of 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.. _sphx_glr_download_checks_gallery_tabular_performance_plot_multi_model_performance_report.py:


.. only :: html

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

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



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

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


.. only:: html

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