Hi, I have been trying to understand about Ensemble Bagged tree classification. I know that it combines a set of trained weak learner models and can predict ensemble response for new data by aggregating predictions from its weak learners. Can you tell me what these weak learners are? What algorithm are they based on to arrive at a prediction? Is it gradient descent , Adaboost or something else? Also, are all the models same and only the data varies between different bags or the models are also based on different algorithms?
I really appreciate your help and time.
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