Which of the following statement(s) is/are true for bagging and boosting? A) Bagging: Weak learners are built in parallel Boosting: Weak learners are built in sequence one after the other B) Bagging: More weight to those weak learners with better performance Boosting: Each weak learner has equal weight in the final prediction. C) Bagging: Samples are drawn from the original dataset with replacement to train each individual weak learner Boosting: Subsequent samples have more weight of those observations

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