Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach


Satoshi Hara, Kohei Hayashi ;
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:77-85, 2018.


Tree ensembles, such as random forests, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enormous complexity. In this study, we propose a method to make a complex tree ensemble interpretable by simplifying the model. Specifically, we formalize the simplification of tree ensembles as a model selection problem. Given a complex tree ensemble, we aim at obtaining the simplest representation that is essentially equivalent to the original one. To this end, we derive a Bayesian model selection algorithm that optimizes the simplified model while maintaining the prediction performance. Our numerical experiments on several datasets showed that complicated tree ensembles were approximated interpretably.

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