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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_model_selection_plot_roc_crossval.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_model_selection_plot_roc_crossval.py:


=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================

Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.

ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X axis. This means that the top left corner of the plot is
the "ideal" point - a false positive rate of zero, and a true positive rate of
one. This is not very realistic, but it does mean that a larger area under the
curve (AUC) is usually better.

The "steepness" of ROC curves is also important, since it is ideal to maximize
the true positive rate while minimizing the false positive rate.

This example shows the ROC response of different datasets, created from K-fold
cross-validation. Taking all of these curves, it is possible to calculate the
mean area under curve, and see the variance of the curve when the
training set is split into different subsets. This roughly shows how the
classifier output is affected by changes in the training data, and how
different the splits generated by K-fold cross-validation are from one another.

.. note::

    See also :func:`sklearn.metrics.roc_auc_score`,
             :func:`sklearn.model_selection.cross_val_score`,
             :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py`,

.. GENERATED FROM PYTHON SOURCE LINES 32-100



.. image:: /auto_examples/model_selection/images/sphx_glr_plot_roc_crossval_001.png
    :alt: Receiver operating characteristic example
    :class: sphx-glr-single-img





.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn import svm, datasets
    from sklearn.metrics import auc
    from sklearn.metrics import plot_roc_curve
    from sklearn.model_selection import StratifiedKFold

    # #############################################################################
    # Data IO and generation

    # Import some data to play with
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    X, y = X[y != 2], y[y != 2]
    n_samples, n_features = X.shape

    # Add noisy features
    random_state = np.random.RandomState(0)
    X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

    # #############################################################################
    # Classification and ROC analysis

    # Run classifier with cross-validation and plot ROC curves
    cv = StratifiedKFold(n_splits=6)
    classifier = svm.SVC(kernel='linear', probability=True,
                         random_state=random_state)

    tprs = []
    aucs = []
    mean_fpr = np.linspace(0, 1, 100)

    fig, ax = plt.subplots()
    for i, (train, test) in enumerate(cv.split(X, y)):
        classifier.fit(X[train], y[train])
        viz = plot_roc_curve(classifier, X[test], y[test],
                             name='ROC fold {}'.format(i),
                             alpha=0.3, lw=1, ax=ax)
        interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
        interp_tpr[0] = 0.0
        tprs.append(interp_tpr)
        aucs.append(viz.roc_auc)

    ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
            label='Chance', alpha=.8)

    mean_tpr = np.mean(tprs, axis=0)
    mean_tpr[-1] = 1.0
    mean_auc = auc(mean_fpr, mean_tpr)
    std_auc = np.std(aucs)
    ax.plot(mean_fpr, mean_tpr, color='b',
            label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
            lw=2, alpha=.8)

    std_tpr = np.std(tprs, axis=0)
    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
    ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
                    label=r'$\pm$ 1 std. dev.')

    ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],
           title="Receiver operating characteristic example")
    ax.legend(loc="lower right")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.287 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_roc_crossval.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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