when I run the
predict_proba method with multiple rows (ndata say) in a multi-class classifier, I do get a
ndata * nclass matrix output as well.
From what I know,
softmax calculates probability distribution over a vector of values. So not sure what
softprob is doing differently.
Can someone clarify the difference? It’s probably very subtle but escapes me
What I am confused about is doesn’t
predict_proba (with softmax) also output a vector of probabilities over the classes?
clf.predict_proba([[...]]) = [[0.2,...0.8], [0.1,...0.4],...]
Isn’t this also outputting a matrix of probabilities? How is that output different from the