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>
> 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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