On Apr 17, 2012, at 15:53 , Alexandre Gramfort wrote: >> I think just moving from a train set to a test set would be problematic for >> small n_samples. > > what do you suggest? >
I agree with your scale_C=None suggestion because it would (in theory) force the user to become aware of what the setting means. For this to work there are a couple of points I'd like to raise: 1. To make progress in the fight with the windmills, the docs should make it clear that the libsvm default is a bad idea, like with a toy example that would break. 2. I fear that dismissive users will start destructive word-of-mouth: "sklearn svms don't work <<correctly>> unless you set scale_C=True", and users would end up setting scale_C to True without reading the docs. I think this needs more than just clarification in the docs. I think we need some sort of "Release notes" or "News" highlight on the front page. A blog post maybe, that should be made viral in some way. SVMs are probably one of our most used modules. Vlad > Alex > > ------------------------------------------------------------------------------ > 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
