The early termination in sklearn.ensemble.AdaBoostClassifier may be too strict under certain scenarios (only 1 base classifier is trained), which greatly hinders the performance of boosting-based ensemble imbalanced learning methods.
It should make more sense to add a parameter that allows the user to decide whether to enable strict early termination.
The early termination in
sklearn.ensemble.AdaBoostClassifier
may be too strict under certain scenarios (only 1 base classifier is trained), which greatly hinders the performance of boosting-based ensemble imbalanced learning methods.It should make more sense to add a parameter that allows the user to decide whether to enable strict early termination.