@scikit-learn developers: Hum... http://www.flickr.com/photos/scriptingnews/3503448168/sizes/o/in/photostream/
The situation is that the authors of libSVM have chosen a solution that leads to inconsistent estimator with bad statistical properties, but works well on many datasets. I think it is wrong, but then, I am worried that this might be a battle that we might not win. On the one hand, we really cannot have C the way the libSVM guy have defined it, because parameter setting by cross-validation will not work. On the other hand, it is clear that people keep tripping over this difference. Should we introduce a different name, that way it forces people to read the docs? We've been going round and round with regards to this issue for a while :) G On Tue, Apr 17, 2012 at 12:39:04PM +0300, Dimitrios Pritsos wrote: > Hello G, > Yes you are right the scale_C should be False for working as expected. > Great because I prefer to work with the latest version. > Thank you G > Dimitrios > On 04/17/2012 12:13 PM, Dimitrios Pritsos wrote: > > Ok I will do that now and I will let you know in 45 min > > On 04/17/2012 12:10 PM, Gael Varoquaux wrote: > >> On Tue, Apr 17, 2012 at 12:08:46PM +0300, Dimitrios Pritsos wrote: > >>> I was running a test using SVC(c=1, kernel='linear') and I found that > >>> for the latest version of sklearn the results are WRONG! > >> What does 'wrong' mean? > >> Something that changed in the scikit, is that the 'c' is scaled by the > >> number of samples by default. Try with 'scale_C=False', and tells us if > >> your results are now 'RIGHT' :). > >> G > >> ------------------------------------------------------------------------------ > >> Better than sec? Nothing is better than sec when it comes to > >> monitoring Big Data applications. Try Boundary one-second > >> resolution app monitoring today. Free. > >> http://p.sf.net/sfu/Boundary-dev2dev > >> _______________________________________________ > >> Scikit-learn-general mailing list > >> [email protected] > >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > > Better than sec? Nothing is better than sec when it comes to > > monitoring Big Data applications. Try Boundary one-second > > resolution app monitoring today. Free. > > http://p.sf.net/sfu/Boundary-dev2dev > > _______________________________________________ > > Scikit-learn-general mailing list > > [email protected] > > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ > Better than sec? Nothing is better than sec when it comes to > monitoring Big Data applications. Try Boundary one-second > resolution app monitoring today. Free. > http://p.sf.net/sfu/Boundary-dev2dev > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Gael Varoquaux Researcher, INRIA Parietal Laboratoire de Neuro-Imagerie Assistee par Ordinateur NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux ------------------------------------------------------------------------------ Better than sec? Nothing is better than sec when it comes to monitoring Big Data applications. Try Boundary one-second resolution app monitoring today. Free. http://p.sf.net/sfu/Boundary-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
