Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a C-SVM of the selected features.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | print(__doc__)from sklearn import svmfrom sklearn.datasets import samples_generatorfrom sklearn.feature_selection import SelectKBest, f_regressionfrom sklearn.pipeline import make_pipeline# import some data to play withX, 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 featuresanova_filter = SelectKBest(f_regression, k=3)# 2) svmclf = 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)
Download Python source code:
feature_selection_pipeline.py
Download IPython notebook:
feature_selection_pipeline.ipynb
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