[scikit-learn] Normalizer, l1 and l2 norms

[Guillaume Lemaître]

Since you are normalizing sample by sample, you don't need information from
the training set to normalize a new sample.
You just need to compute the norm of this new sample.

On Tue, 24 Sep 2019 at 13:41, Sole Galli <solegalli1 at gmail.com> wrote:

> Hello team,
>
> Quick question respect to the Normalizer().
>
> My understanding is that this transformer divides the values (rows) of a
> vector by the vector euclidean (l2) or manhattan distances (l1).
>
> From the sklearn docs, I understand that the Normalizer() does not learn
> the distances from the train set and stores them. It rathers normalises the
> data according to distance the data set presents, which could be or not,
> the same in test and train.
>
> Am I understanding this correctly?
>
> If so, what is the reason not to store these parameters in the Normalizer
> and use them to scale future data?
>
> If not getting it right, what am I missing?
>
> Many thanks and I will appreciate if you have an article on this to share.
>
> Cheers
>
> Sole
>
>
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-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
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