Hi Andy,
The second question was about using random forest for feature selection. But I
guess it'd be the same, thing, using transform() function
Thank you,
From: Andy [mailto:t3k...@gmail.com]
Sent: Thursday, January 29, 2015 12:18 PM
To: scikit-learn-general@lists.sourceforge.net
Subject: 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, 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,
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