.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "checks_gallery/tabular/performance/plot_roc_report.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_roc_report.py>`
        to download the full example code

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

.. _sphx_glr_checks_gallery_tabular_performance_plot_roc_report.py:


ROC Report
**********

.. GENERATED FROM PYTHON SOURCE LINES 8-10

Imports
=======

.. GENERATED FROM PYTHON SOURCE LINES 10-27

.. 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.performance import RocReport


    def custom_formatwarning(msg, *args, **kwargs):
        return str(msg) + '\n'

    warnings.formatwarning = custom_formatwarning








.. GENERATED FROM PYTHON SOURCE LINES 28-30

Generating data
===============

.. GENERATED FROM PYTHON SOURCE LINES 30-42

.. code-block:: default


    iris = load_iris(as_frame=True)
    clf = LogisticRegression(penalty='none')
    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 43-45

Running ``roc_report`` Check
============================

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: default


    check = RocReport()








.. GENERATED FROM PYTHON SOURCE LINES 49-51

.. code-block:: default


    check.run(ds, clf)





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

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            The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined
            as sensitivity + specificity - 1
            </i></span></div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** ( 0 minutes  0.035 seconds)


.. _sphx_glr_download_checks_gallery_tabular_performance_plot_roc_report.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_roc_report.py <plot_roc_report.py>`



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

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


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

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