Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning
This work addresses the need for interpretable AI models in safety-critical applications by improving methods to optimize the size and accuracy of decision trees.
The paper tackled the problem of learning compact decision trees to balance interpretability and performance, proving that a fully observable Markov decision process is sufficient for this trade-off and demonstrating competitive results on classical classification datasets.
Interpretability of AI models allows for user safety checks to build trust in these models. In particular, decision trees (DTs) provide a global view on the learned model and clearly outlines the role of the features that are critical to classify a given data. However, interpretability is hindered if the DT is too large. To learn compact trees, a Reinforcement Learning (RL) framework has been recently proposed to explore the space of DTs. A given supervised classification task is modeled as a Markov decision problem (MDP) and then augmented with additional actions that gather information about the features, equivalent to building a DT. By appropriately penalizing these actions, the RL agent learns to optimally trade-off size and performance of a DT. However, to do so, this RL agent has to solve a partially observable MDP. The main contribution of this paper is to prove that it is sufficient to solve a fully observable problem to learn a DT optimizing the interpretability-performance trade-off. As such any planning or RL algorithm can be used. We demonstrate the effectiveness of this approach on a set of classical supervised classification datasets and compare our approach with other interpretability-performance optimizing methods.