Hi Giles, thanks for the suggestion!
Training a regression tree would require sticking some kind of probability normaliser at the end to ensure proper probabilities, this might somehow hurt sharpness or calibration. Unfortunately, one of the things I am trying to do with this is moving away from RF and they humongous memory requirements… Anyway, I think I have a fairly good idea on how to modify the MLPClassifier to get what I need. When I get around to do it I’ll drop a line to see if there might be any interest on pushing the code upstream. Cheers, J > On 13 Mar 2017, at 07:43, Gilles Louppe <[email protected]> wrote: > > Hi Javier, > > In the particular case of tree-based models, you case use the soft > labels to create a multi-output regression problem, which would yield > an equivalent classifier (one can show that reduction of variance and > the gini index would yield the same trees). > > So basically, > > reg = RandomForestRegressor() > reg.fit(X, encoded_y) > > should work. > > Gilles _______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
