30 lines
799 B
Python
30 lines
799 B
Python
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"""
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==================
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Pipeline Anova SVM
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==================
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Simple usage of Pipeline that runs successively a univariate
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feature selection with anova and then a C-SVM of the selected features.
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"""
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print(__doc__)
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from sklearn import svm
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from sklearn.datasets import samples_generator
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from sklearn.feature_selection import SelectKBest, f_regression
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from sklearn.pipeline import make_pipeline
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# import some data to play with
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X, y = samples_generator.make_classification(
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n_features=20, n_informative=3, n_redundant=0, n_classes=4,
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n_clusters_per_class=2)
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# ANOVA SVM-C
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# 1) anova filter, take 3 best ranked features
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anova_filter = SelectKBest(f_regression, k=3)
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# 2) svm
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clf = svm.SVC(kernel='linear')
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anova_svm = make_pipeline(anova_filter, clf)
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anova_svm.fit(X, y)
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anova_svm.predict(X)
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