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 <solegal...@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 > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/
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