[scikit-learn] Use of Scaler with LassoCV, RidgeCV

[Dale T Smith]

Hmm. I would scale the training data, and then use the same scaling on the test and validation data. This isn’t quite what you asked, but it’s close and does involve transformations and pipelines. Perhaps you can modify according to your use case, introducing the scaling before PolynomialFeatures is called.

https://www.datarobot.com/blog/regularized-linear-regression-with-scikit-learn/

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Dale Smith | Macy's Systems and Technology | IFS eCommerce | Data Science
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From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com at python.org] On Behalf Of Brenet, Yoann
Sent: Tuesday, September 13, 2016 8:16 AM
To: scikit-learn at python.org
Subject: [scikit-learn] Use of Scaler with LassoCV, RidgeCV

⚠ EXT MSG:
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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