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Interpretable decision trees through MaxSAT.

In Artificial intelligence review

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.

Alòs Josep, Ansótegui Carlos, Torres Eduard

2022-Dec-27

Decision trees, Explainable AI, MaxSAT