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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_feature_selection_plot_rfe_with_cross_validation.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_feature_selection_plot_rfe_with_cross_validation.py:


===================================================
Recursive feature elimination with cross-validation
===================================================

A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.

.. GENERATED FROM PYTHON SOURCE LINES 9-43



.. image:: /auto_examples/feature_selection/images/sphx_glr_plot_rfe_with_cross_validation_001.png
    :alt: plot rfe with cross validation
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Optimal number of features : 3






|

.. code-block:: default

    print(__doc__)

    import matplotlib.pyplot as plt
    from sklearn.svm import SVC
    from sklearn.model_selection import StratifiedKFold
    from sklearn.feature_selection import RFECV
    from sklearn.datasets import make_classification

    # Build a classification task using 3 informative features
    X, y = make_classification(n_samples=1000, n_features=25, n_informative=3,
                               n_redundant=2, n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, random_state=0)

    # Create the RFE object and compute a cross-validated score.
    svc = SVC(kernel="linear")
    # The "accuracy" scoring is proportional to the number of correct
    # classifications

    min_features_to_select = 1  # Minimum number of features to consider
    rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),
                  scoring='accuracy',
                  min_features_to_select=min_features_to_select)
    rfecv.fit(X, y)

    print("Optimal number of features : %d" % rfecv.n_features_)

    # Plot number of features VS. cross-validation scores
    plt.figure()
    plt.xlabel("Number of features selected")
    plt.ylabel("Cross validation score (nb of correct classifications)")
    plt.plot(range(min_features_to_select,
                   len(rfecv.grid_scores_) + min_features_to_select),
             rfecv.grid_scores_)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  3.016 seconds)


.. _sphx_glr_download_auto_examples_feature_selection_plot_rfe_with_cross_validation.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/feature_selection/plot_rfe_with_cross_validation.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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