python-scikit-learnFeature selection using RFE
from sklearn import datasets, svm, feature_selection
X, y = datasets.make_friedman1(n_samples=50, n_features=10)
selector = feature_selection.RFECV(svm.SVR(kernel="linear"), step=1, cv=5)
selector = selector.fit(X, y)
ranks = selector.ranking_ctrl + cfrom sklearn importimport module from scikit-learn | .make_friedman1(creates Friedman #1 regression dataset |
.RFECV(creates RFE feature selection model | svm.SVR(we use SVR model as estimator for RFE |
.fit(train model | selector.ranking_returns features rankings |
Usage example
from sklearn import datasets, svm, feature_selection
X, y = datasets.make_friedman1(n_samples=50, n_features=10)
selector = feature_selection.RFECV(svm.SVR(kernel="linear"), step=1, cv=5)
selector = selector.fit(X, y)
print(selector.ranking_)output
[1 1 3 1 1 1 1 2 1 1]
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