In these approaches we first fit a deep (likely overfitting) tree to the data, as tightly as we can, and then repeatedly bootstrap that model, by randomly selecting different combinations and orders of the splits (i.e. the branching nodes) from that tree. We hope that by trying different splits in different orders, with replacement, and averaging predictions of the resulting models, we can obtain a better overall prediction