.. _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.



.. image:: /auto_examples/feature_selection/images/sphx_glr_plot_rfe_with_cross_validation_001.png
    :align: center


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

 Out::

      Optimal number of features : 3




|


.. code-block:: python

    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
    rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),
                  scoring='accuracy')
    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(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
    plt.show()

**Total running time of the script:**
(0 minutes 2.340 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_rfe_with_cross_validation.ipynb <plot_rfe_with_cross_validation.ipynb>`