>>> [(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. :) Ok, but then the second should sum to one ;)
> > 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! That is exactly what I meant. So just do that, it will give you exactly the same as OvR. You should train with loss="log" for it to be "meaningful". Sorry, I was brief as I should have been working ;) ------------------------------------------------------------------------------ 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
