Note
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Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a C-SVM of the selected features.
Out:
precision recall f1-score support 0 0.75 0.50 0.60 6 1 0.60 1.00 0.75 6 2 0.67 0.80 0.73 5 3 1.00 0.62 0.77 8 micro avg 0.72 0.72 0.72 25 macro avg 0.75 0.73 0.71 25 weighted avg 0.78 0.72 0.72 25
from sklearn import svm from sklearn.datasets import samples_generator from sklearn.feature_selection import SelectKBest, f_regression from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report print(__doc__) # 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) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) # 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_train, y_train) y_pred = anova_svm.predict(X_test) print(classification_report(y_test, y_pred))
Total running time of the script: ( 0 minutes 0.008 seconds)
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http://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html