how train AdaBoost M1 weak estimators? I'm trying to implement AdaBoost.M1 as explained in Boosting: Foundations and Algorithms by Robert E. Schapire and Y. Freund. The problem is that I don't understand at each iteration t the estimator for which label should be trained. Here is the pseudocode of the algorithm from the book:


AdaBoost is a technique to ensemble weak learners; the actual learner used can be anything that supports sample weights (possibly by just resampling according to those weights).  The most common learner for this is a decision tree, usually very shallow: very often only depth 1, a "decision stump."
