What's the difference between predict_proba and decision_function in scikit-learn?

Question

I\'m studying a scikit-learn example (Classifier comparison) and got confused with predict_proba and decision_function.

They plot the classific

Answer 1

The latter, predict_proba is a method of a (soft) classifier outputting the probability of the instance being in each of the classes.

The former, decision_function, finds the distance to the separating hyperplane. For example, a(n) SVM classifier finds hyperplanes separating the space into areas associated with classification outcomes. This function, given a point, finds the distance to the separators.

I'd guess that predict_prob is more useful in your case, in general - the other method is more specific to the algorithm.