It's here (and it's old and probably out of date):
https://github.com/scikit-learn/scikit-learn/issues/1626
On 09/13/2016 08:45 AM, Brenet, Yoann wrote:
Hi Sebastian,
Many thanks, that's what I was thinking I should be doing, so thanks a lot for
confirming that was the way to go.
Really appreciate the help,
Yoann
Date: Tue, 13 Sep 2016 08:33:52 -0400
From: Sebastian Raschka <[email protected]>
To: Scikit-learn user and developer mailing list
<[email protected]>
Subject: Re: [scikit-learn] Use of Scaler with LassoCV, RidgeCV
Message-ID: <[email protected]>
Content-Type: text/plain; charset=utf-8
Hi, Yoann,
when I understand correctly, you want to apply the scaling to each iteration in
cross-validation (i.e., the recommended way to do it)? Here, you could use the
make_pipeline function, which will call fit on each training fold and call
transform on each test fold:
from sklearn.preprocessing import StandardScaler from sklearn.pipeline import
make_pipeline from sklearn.cross_validation import cross_val_score from
sklearn.linear_model import Ridge
pipe = make_pipeline(StandardScaler(), Ridge()) cross_val_score(pipe, X, y)
You can think of ?pipe? as an Ridge estimator with a StandardScaler attached to
it.
Best,
Sebastian
On Sep 13, 2016, at 8:16 AM, Brenet, Yoann <[email protected]> wrote:
Hi all,
I was trying to use scikit-learn LassoCV/RidgeCV while applying a 'StandardScaler' on each fold set. I do not want to apply the scaler before the cross-validation to avoid leakage but I cannot figure out how I am supposed to do that with LassoCV/RidgeCV.
Is there a way to do this ? Or should I create a pipeline with Lasso/Ridge and 'manually' search for the hyper-parameters (using GridSearchCV for instance) ?
Many thanks.
Yoann
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