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arXiv · 2110.13854

Interpretable Decision Trees Through MaxSAT

Josep Alos·Carlos Ansotegui·Eduard Torres
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Abstract

We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn.

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Josep Alos, Carlos Ansotegui, Eduard Torres. 2021-10-26. Interpretable Decision Trees Through MaxSAT. https://arxiv.org/abs/2110.13854

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