> It's probably better to train a linear classifier on the text features > alone and a second (potentially non linear classifier such as GBRT or > ExtraTrees) on the predict_proba outcome of the text classifier + your > additional low dim features. > > This is some kind of stacking method (a sort of ensemble method). It > should make the text features not overwhelm the final classifier if > the other features are informative.
Hey Olivier! Thanks for the hints. I just tried it, but unfortunately the results are much worse than just using my textual features alone. just to be sure if I am doing it right: At first I create my textual features using a vectorizer. Then I fit a linear SVC on these features (training data ofc) and use predict_proba for my training samples again resulting in a probability distribution of dimension 7 (I have 7 classes). Then I append my additional features (those are 15) and fit another classifier on the new data. (I tried several scaling/normalizing ideas without improvement) I do the same procedure for test data. (Btw I do cross val) While I get 0.85 f1 score for just using textual data the combined approach results in only 0.4. Regards, Philipp ------------------------------------------------------------------------------ LogMeIn Rescue: Anywhere, Anytime Remote support for IT. Free Trial Remotely access PCs and mobile devices and provide instant support Improve your efficiency, and focus on delivering more value-add services Discover what IT Professionals Know. Rescue delivers http://p.sf.net/sfu/logmein_12329d2d _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
