>From the documentation:
"Feature selection is usually used as a pre-processing step before doing the
actual learning. The recommended way to do this in scikit-learn is to use a
sklearn.pipeline.Pipeline<http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline>:
clf = Pipeline([
('feature_selection', LinearSVC(penalty="l1")),
('classification', RandomForestClassifier())
])
clf.fit(X, y)
In this snippet we make use of a
sklearn.svm.LinearSVC<http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC>
to evaluate feature importances and select the most relevant features."
How many features get selected? Is that configurable?
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