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 which had relatively higher errors in previous weak learners

D) Bagging: Random forest is a special type of bagging technique Boosting: Adaboost is a special type of boosting technique