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 > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn ------------------------------ Subject: Digest Footer _______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn ------------------------------ End of scikit-learn Digest, Vol 6, Issue 15 ******************************************* _______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
