Adaptive Split Balancing for Optimal Random Forest

arXiv:2402.11228v1 Announce Type: cross
Abstract: While random forests are commonly used for regression problems, existing methods often lack adaptability in complex situations or lose optimality under simple, smooth scenarios. In this study, we introduce the adaptive split balancing forest (ASBF), capable of learning tree representations from data while simultaneously achieving minimax optimality under the Lipschitz class. To exploit higher-order smoothness levels, we further propose a localized version that attains the minimax rate under the H\"older class $\mathcal{H}^{q,\beta}$ for any $q\in\mathbb{N}$ …

abstract adaptability arxiv cs.lg data forests math.st minimax random random forests regression simple stat.me stat.ml stat.th study tree type