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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