A related question was recently asked recently on Stack Overflow. Does this
help?
http://stackoverflow.com/questions/16137816/scikit-learn-preprocessing-svm-with-multiple-classes-in-a-pipeline
On Tue, May 28, 2013 at 12:18 AM, Gianni Iannelli <giannicrist...@msn.com>wrote:
> Found it! But now it has a different name: StandardScaler.
>
> The documentation is here:
> http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler
>
> Furthermore, I was reading the documentation:
>
> Centering and scaling happen indepently on each feature by computing the
> relevant statistics on the samples in the training set. Mean and standard
> deviation are then stored to be used on later data using the transform
> method.
>
>
> Could you please exlpain to me what its the point to store Mean and
> Standard Deviation? It's not so clear to me. And how the transform is made?
> Sorry for my lower knowledge level about this stuffs...I wanna be sure to
> understand everything.
>
> Thanks All,
> Solimyr
>
>
>
>
> > From: l.j.buiti...@uva.nl
> > Date: Mon, 27 May 2013 15:44:23 +0200
>
> > To: scikit-learn-general@lists.sourceforge.net
> > Subject: Re: [Scikit-learn-general] SVM - Scaling data or not?
> >
> > 2013/5/27 Gianni Iannelli <giannicrist...@msn.com>:
> > > I was trying the scaler() but Python say to me that doesn't exist.
> Could
> > > you please tell me where I wrong? And I could not find also the
> transform
> > > method.
> >
> > from sklearn.preprocessing import Scaler
> >
> > (with a capital S)
> >
> > --
> > Lars Buitinck
> > Scientific programmer, ILPS
> > University of Amsterdam
> >
> >
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