First I scaled the complete data-set and then splitting it in test and
train data.
On 8 July 2014 16:13, Lars Buitinck <larsm...@gmail.com> wrote:
> 2014-07-08 16:00 GMT+02:00 Michael Eickenberg <
> michael.eickenb...@gmail.com>:
> > That totally depends on your data. Here it looks like you are scaling
> down a
> > feature that captures a lot of the variation you are looking for, thus
> > making it less important with respect to the other features in the
> euclidean
> > distance. You could try selecting important features beforehand. But they
> > may be non-coordinate directions in your feature space as well.
>
> It also depends on whether you did the scaling right. How did you
> scale the test set?
>
>
> ------------------------------------------------------------------------------
> Open source business process management suite built on Java and Eclipse
> Turn processes into business applications with Bonita BPM Community Edition
> Quickly connect people, data, and systems into organized workflows
> Winner of BOSSIE, CODIE, OW2 and Gartner awards
> http://p.sf.net/sfu/Bonitasoft
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
Open source business process management suite built on Java and Eclipse
Turn processes into business applications with Bonita BPM Community Edition
Quickly connect people, data, and systems into organized workflows
Winner of BOSSIE, CODIE, OW2 and Gartner awards
http://p.sf.net/sfu/Bonitasoft
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
