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

.. only:: html

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

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

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

.. _sphx_glr_checks_gallery_tabular_performance_plot_calibration_score.py:


Calibration Score
*****************

.. GENERATED FROM PYTHON SOURCE LINES 8-27

.. code-block:: default


    import warnings

    import pandas as pd
    from sklearn.datasets import load_iris
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split

    from deepchecks.tabular import Dataset
    from deepchecks.tabular.checks import CalibrationScore
    from deepchecks.tabular.datasets.classification import adult


    def custom_formatwarning(msg, *args, **kwargs):
        # ignore everything except the message
        return str(msg) + '\n'

    warnings.formatwarning = custom_formatwarning








.. GENERATED FROM PYTHON SOURCE LINES 28-37

Binary Classification
=====================
Load data
---------
The dataset is the adult dataset which can be downloaded from the UCI machine
learning repository.

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.

.. GENERATED FROM PYTHON SOURCE LINES 37-44

.. code-block:: default


    from urllib.request import urlopen

    from sklearn.preprocessing import LabelEncoder

    label_name = 'income'








.. GENERATED FROM PYTHON SOURCE LINES 45-50

.. code-block:: default


    from deepchecks.tabular import Dataset

    train_ds, test_ds = adult.load_data()








.. GENERATED FROM PYTHON SOURCE LINES 51-53

.. code-block:: default


    model = adult.load_fitted_model()







.. GENERATED FROM PYTHON SOURCE LINES 54-58

.. code-block:: default


    check = CalibrationScore()
    check.run(test_ds, model)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

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        src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"
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                },
                function() {console.log('Failed to load plotly')}
            );
        } else {
            console.log('requirejs is not present');
        }
    </script>
    <h4><b>Calibration Metric</b></h4><p>Calculate the calibration curve with brier score for each class.</p><h5><b>Additional Outputs</b></h5><div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. 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plots (reliability curve)"},"width":700,"height":500,"yaxis":{"title":{"text":"Fraction of positives"}},"xaxis":{"title":{"text":"Mean predicted value"}}},                        {"responsive": true}                    ).then(function(){
                            
    var gd = document.getElementById('ee9195ef-516e-4f00-95b7-407be915a6d2');
    var x = new MutationObserver(function (mutations, observer) {{
            var display = window.getComputedStyle(gd).display;
            if (!display || display === 'none') {{
                console.log([gd, 'removed!']);
                Plotly.purge(gd);
                observer.disconnect();
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                            })                };                });            </script>        </div><div>The Brier score metric may be used to assess how well a classifier is calibrated. For more info, please visit https://en.wikipedia.org/wiki/Brier_score</div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-61

Multi-class classification
==========================

.. GENERATED FROM PYTHON SOURCE LINES 61-73

.. code-block:: default


    iris = load_iris(as_frame=True)
    clf = LogisticRegression()
    frame = iris.frame
    X = iris.data
    y = iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=55)
    clf.fit(X_train, y_train)
    ds = Dataset(pd.concat([X_test, y_test], axis=1), 
                features=iris.feature_names,
                label='target')








.. GENERATED FROM PYTHON SOURCE LINES 74-77

.. code-block:: default


    check = CalibrationScore()
    check.run(ds, clf)





.. raw:: html

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        }
    </script>
    <h4><b>Calibration Metric</b></h4><p>Calculate the calibration curve with brier score for each class.</p><h5><b>Additional Outputs</b></h5><div>Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. 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.. _sphx_glr_download_checks_gallery_tabular_performance_plot_calibration_score.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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