Suppose you have a classification task y~X with (n_samples,m_features). A colleague told me that it is correct to run r different classifiers to predict y based on X and then use the probabilities given for each classifier as a new matrix Xnew (n_samples_probabilities,r_columns) to train a new classifier y~Xnew
My questions are:
1) Is this something reasonable to apply?
2) If so, is there any mathematical support on this method?