Interpretable decision trees through MaxSAT
Josep Alòs1, Carlos Ansótegui1, Eduard Torres1
1Logic & Optimization Group (LOG), University of Lleida, Lleida, Spain.
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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