Forest-RK: A New Random Forest Induction Method

In this paper we present our work on the parametrization of Random Forests (RF), and more particularly on the number K of features randomly selected at each node during the tree induction process. It has been shown that this hyperparameter can play a significant role on performance. However, the choice of the value of K is usually made either by a greedy search that tests every possible value to choose the optimal one, either by choosing a priori one of the three arbitrary values commonly used in the literature. With this work we show that none of those three values is always better than the others. We thus propose an alternative to those arbitrary choices of K with a new ”push-button” RF induction method, called Forest-RK, for which K is not an hyperparameter anymore. Our experimentations show that this new method is at least as statistically accurate as the original RF method with a default K setting.