On 12/04/2012 02:05 AM, Afik Cohen wrote: > Andreas Mueller <amueller@...> writes: > >> On 12/03/2012 09:39 PM, Afik Cohen wrote: >>> No, we aren't doing multi-label classification, just multiclass. He was > saying >>> we could just use SGDClassifier directly, which is true, but AFAIK there is > no >>> way to get good prediction probability outputs on a per-class basis unless > you >>> train binary classifiers by wrapping it in a OneVsRestClassifier() call... > or >>> is there? >> Hm it seems that the predict_proba is not implemented for multi-class. >> I thought someone had done that already, sorry. >> >> It does train OVR and you can just take the sigmoid of the decision function >> and than normalize. >> Not sure why that is not implemented, I think we could basically >> copy and paste that from logistic regression. >> > > 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.
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