Interpretable Decision Tree Search as a Markov Decision Process

arXiv:2309.12701v3 Announce Type: replace
Abstract: Finding an optimal decision tree for a supervised learning task is a challenging combinatorial problem to solve at scale. It was recently proposed to frame the problem as a Markov Decision Problem (MDP) and use deep reinforcement learning to tackle scaling. Unfortunately, these methods are not competitive with the current branch-and-bound state-of-the-art. We propose instead to scale the resolution of such MDPs using an information-theoretic tests generating function that heuristically, and dynamically for every state, …

abstract arxiv cs.lg decision markov process reinforcement reinforcement learning scale scaling search solve supervised learning tree type