> > Will this let us run SGDClassifier and show us per-class probability outputs? > > Again, that's the only reason we've been using OneVsRestClassifier. Let me > > explain what I mean by per-class probability, just in case it isn't clear: > > > > SGDClassifier's predict_proba() returns probability of belonging to each class, > > so if for example there are five classes, it will return something like > > [0.5, 0.3, 0.1, 0.05, 0.05]. > > For our use case, though, we need a negative/positive probability display, i.e. > > [(0.4, 0.5), (0.7, 0.3), (0.8, 0.2), (0.9, 0.1), (0.6, 0.4)] for five classes > > showing the probability that the input does not belong/does belong to that > > class, respectively. > > > Yes, if you don't normalize. > You are aware that this is inconsistent when you are doing multi-class, > not multi-label, right? > It there is only one correct label, it can not be label 2 with > probability .7 and label 3 with probability .8. >
Those are "does not belong"/"does belong" pairs; the first number is the probability that the input is NOT part of the class. :) I'm starting to understand what you mean; the "[(0.4, 0.5), (0.7, 0.3), ..." values are achieved by taking the sigmoid of each value in the decision function, right? And if I then normalize that, I'll get something in the form of "[0.5, 0.3, 0.1, 0.05, 0.05]"? Apologies, I'm still new to some of this stuff! Thanks, Afik ------------------------------------------------------------------------------ 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 [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
