Simple reason. Thanks Hemanth
On Tue, Oct 8, 2019, 5:26 PM Guillaume Lemaître <g.lemaitr...@gmail.com> wrote: > You all apply them before to use any machine learning algorithm. They are > preprocessing methods. > > Sent from my phone - sorry to be brief and potential misspell. > *From:* hemanth.ge...@gmail.com > *Sent:* 8 October 2019 14:49 > *To:* scikit-learn@python.org > *Reply to:* scikit-learn@python.org > *Subject:* [scikit-learn] Regarding design decision for putting Data > Scaler and Feature Transformers under same module > > Hi Team, > > I'm beginner in using sklearn library. I have doubt regarding reason for > putting Data scalers like StandardScaler, RobustScaler etc and feature > transformers like QuantileTransformer, PolynomialFeatures in preprocessing > module ? What relationship made to put them together? > > Thanks > Hemanth > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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