Weighing each label in multi-label classification If, in addition to predicting labels using a multi-label classifier, I'm interested in predicting the weight of each label, what approach should be taken? To give an example, let's say I'm trying to predict movie genres from their plot and for a movie like Terminator the classifier predicts ['Sci-Fi', 'Action'], then is it also possible to estimate the proportion of those genres in that movie, like it's 70% Action and 30% Sci-Fi?

Multi-label classifier does give the probability for each class; is it a good idea to just normalize those probabilities and use them as the weights?
Yes, of course, this technique exists. In XGBoost, for instance, you can change the objective function to the multi:softprob which in specifics does:


  multi:softprob: same as softmax, but output a vector of ndata *
  nclass, which can be   further reshaped to ndata * nclass matrix. The
  result contains predicted probability of each data point belonging to
  each class.


From my memory, if it answers your question I think it rounds up to 1.