.. _sphx_glr_auto_examples_feature_selection_feature_selection_pipeline.py: ================== Pipeline Anova SVM ================== Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a C-SVM of the selected features. .. code-block:: python print(__doc__) from sklearn import svm from sklearn.datasets import samples_generator from sklearn.feature_selection import SelectKBest, f_regression from sklearn.pipeline import make_pipeline # import some data to play with X, y = samples_generator.make_classification( n_features=20, n_informative=3, n_redundant=0, n_classes=4, n_clusters_per_class=2) # ANOVA SVM-C # 1) anova filter, take 3 best ranked features anova_filter = SelectKBest(f_regression, k=3) # 2) svm clf = svm.SVC(kernel='linear') anova_svm = make_pipeline(anova_filter, clf) anova_svm.fit(X, y) anova_svm.predict(X) **Total running time of the script:** (0 minutes 0.000 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`feature_selection_pipeline.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`feature_selection_pipeline.ipynb `