From naive trees to Random Forests: A general approach for proving
consistency of tree-based methods
From naive trees to Random Forests: A general approach for proving
consistency of tree-based methods
Tree-based methods such as Random Forests are learning algorithms that have become an integral part of the statistical toolbox. The last decade has shed some light on theoretical properties such as their consistency for regression tasks. However, the usual proofs assume normal error terms as well as an additive regression …