[scikit-learn] Regarding design decision for putting Data Scaler and Feature Transformers under same module

Hemanth Kota hemanth.genie at gmail.com
Tue Oct 8 07:59:29 EDT 2019


Simple reason.

Thanks
Hemanth


On Tue, Oct 8, 2019, 5:26 PM Guillaume Lemaître <g.lemaitre58 at 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.genie at gmail.com
> *Sent:* 8 October 2019 14:49
> *To:* scikit-learn at python.org
> *Reply to:* scikit-learn at 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
>
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