Review for NeurIPS paper: Margins are Insufficient for Explaining Gradient Boosting

Weaknesses: UPDATE: I read the author's reply and I do not agree. In this text, I will focus on the two-class problem, {-1, 1}, for simplicity. First, GB combines regressors, and not classifiers, and their outputs cannot be normalized as classifiers. Second, the training of GB cannot be unlinked from the sigmoid as the pseudo-residuals are computed as the sigmoid times the class (Friedman 1999, section 4.5). In fact the output of the raw function of GB, that is F(x), tends to the log-odds ratio of the two classes.