certain threshold, that classifier is dropped. Margineantu & Dietterich suggested an alternative criterion for trimming: weak classifiers should be selected such that the diversity of the ensemble is maximized. If two weak learners produce very similar outputs, efficiency can be improved by removing one of them and increasing the coefficient of the remaining weak learner. See also Bootstrap aggregating CoBoosting BrownBoost Gradient boosting References Further reading original paper of Yoav Freund and Robert E.Schapire where AdaBoost is first introduced. On the margin explanation of boosting algorithm. On the doubt about margin explanation of boosting. Classification algorithms Ensemble learning