Am 23.01.2013 16:47, schrieb Philipp Singer: > Hey, > > That's what I originally thought, but then I tried it with just using > LinearSVC and it magically worked for my sample dataset, really > interesting. I think it is working now properly. I'm pretty sure it shouldn't. > What I am asking myself is how exactly the decision is made for the > multilabel prediction. Is there some way of influencing it? For example > sometimes it predicts zero classes and sometimes several. OneVsRestClassifier does - surprise - one-vs-rest classification. This means there is a binary classifier pre class. If none of these predict class presence, no class is predicted... You could try using different thresholds on the decision_function, if you want more predictions or something... > Is it also possible to pass a MultinomialNB to the OVR classifier? Or > would I just use the predict_proba output and then decide myself how > many and which labels I would predict? > You can use the OVR classifier with any classifier that provides predict_proba or decision_function - which is basically all in sklearn. It doesn't do anything very smart but it takes the work off you and is well tested.
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