38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
"""
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===================================================
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Recursive feature elimination with cross-validation
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===================================================
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A recursive feature elimination example with automatic tuning of the
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number of features selected with cross-validation.
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"""
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print(__doc__)
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import matplotlib.pyplot as plt
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from sklearn.svm import SVC
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from sklearn.model_selection import StratifiedKFold
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from sklearn.feature_selection import RFECV
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from sklearn.datasets import make_classification
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# Build a classification task using 3 informative features
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X, y = make_classification(n_samples=1000, n_features=25, n_informative=3,
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n_redundant=2, n_repeated=0, n_classes=8,
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n_clusters_per_class=1, random_state=0)
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# Create the RFE object and compute a cross-validated score.
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svc = SVC(kernel="linear")
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# The "accuracy" scoring is proportional to the number of correct
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# classifications
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rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),
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scoring='accuracy')
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rfecv.fit(X, y)
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print("Optimal number of features : %d" % rfecv.n_features_)
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# Plot number of features VS. cross-validation scores
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plt.figure()
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plt.xlabel("Number of features selected")
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plt.ylabel("Cross validation score (nb of correct classifications)")
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plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
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plt.show()
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