What is double bagging in ensemble learning?

Double bagging applies bagging twice to further reduce variance and improve stability.

Double bagging is a hierarchical ensemble technique where bagging is applied at two levels: first to create multiple base learners, and then again on their outputs or on subsets of these learners. This increases randomness and diversity significantly. However, it also increases computational cost and may lead to diminishing returns if overused.