bject: Re: [Scikit-learn-general] select number of features to keep
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, Rob
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, a
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
> wrote:
>
> When using a feature selection algorithm in a pipeline, for example
> clf = Pipeline([
>
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, fo
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
