Interpretable Decision Tree Search as a Markov Decision Process

• URL: http://arxiv.org/abs/2309.12701v4
• Date: Thu, 13 Jun 2024 11:33:26 GMT
• Title: Interpretable Decision Tree Search as a Markov Decision Process
• Authors: Hector Kohler, Riad Akrour, Philippe Preux,
• Abstract summary: Finding an optimal decision tree for a supervised learning task is a challenging 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. We propose instead to scale the resolution of such MDPs using an information-theoretic tests generating function that generates for every state.
• Score: 8.530182510074983
• License: http://creativecommons.org/licenses/by/4.0/
• 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, limits the set of admissible test actions to a few good candidates. As a solver, we show empirically that our algorithm is at the very least competitive with branch-and-bound alternatives. As a machine learning tool, a key advantage of our approach is to solve for multiple complexity-performance trade-offs at virtually no additional cost. With such a set of solutions, a user can then select the tree that generalizes best and which has the interpretability level that best suits their needs, which no current branch-and-bound method allows.

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