As far as i understand, weak learners of AdaBoost should never yield a error rate > 0.5
After training one, i only receive error rates above 0.5. How is that even possible? The AdaBoost Tree still gives quite good results, but all learners weights should be zero, so it should fail. Also the trees get worse from iteration to iteration
is it possible that my threshhold for the error rate instead is 0.9 (accuracy 0.1), as i got 10 classes and literature mostly focusses on binary cases?
from sklearn.ensemble import AdaBoostClassifier adaboost_tree = AdaBoostClassifier( DecisionTreeClassifier(max_depth=max_d), n_estimators=estimators, learning_rate=1, algorithm='SAMME') adaboost_tree.fit(data_train, labels_train)