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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