2012/2/13 Lars Buitinck <[email protected]>: > Hi all, > > After reading some papers on (approximate) polynomial kernels for NLP > applications, I got curious and decided to do some quick experiments. > I modified the 20 newsgroups example to benchmark vanilla SVC instead > of LinearSVC with linear, quadratic and cubic kernels. I was quite > surprised at the results. > > For reference, LinearSVC(C=1000, loss=l2, penalty=l2) obtains an > F1-score of 0.896 on the default set of four document classes. > > I replaced this with > > params = {'C': [.01, .1, 1, 10, 100, 1000]} > GridSearchCV(SVC(kernel='linear'), params, score_func=metrics.f1_score)
I don't know for the polynomial kernel part but since C is scale according to the number of sample, C=1e4 or more is required for text classification. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
