.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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. .. image:: /auto_examples/feature_selection/images/sphx_glr_plot_rfe_with_cross_validation_001.png :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 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.984 seconds) **Estimated memory usage:** 8 MB .. _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:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_rfe_with_cross_validation.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_rfe_with_cross_validation.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_