That is a common way to do it, though not the default behavior of
LinearSVC IIRC.
On 01/29/2015 12:54 PM, Sebastian Raschka wrote:
A naive but related question:
doesn't the l1 norm allow for 0-coefficients? That would be one way to
get rid of the "not so useful" features.
On Jan 29, 2015, at 11:55 AM, Pagliari, Roberto
<rpagli...@appcomsci.com <mailto:rpagli...@appcomsci.com>> 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,
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