For LinearSVC, see the docs: http://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC.transform
I don't understand the second part of your question. On 01/29/2015 11:55 AM, Pagliari, Roberto wrote:
When using a feature selection algorithm in a pipeline, for example clf =Pipeline([ ('feature_selection', LinearSVC(penalty="l1")), ('classification', RandomForestClassifier()) ]) clf.fit(X, y)or even a random forest, for that matter, how does sklearn know how many features to keep?Thank you, ------------------------------------------------------------------------------ Dive into the World of Parallel Programming. The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------ Dive into the World of Parallel Programming. The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/
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