Interpretable Decision Trees Through MaxSAT
This work addresses the challenge of balancing interpretability and performance in decision trees for machine learning practitioners, though it appears incremental as it builds on prior approaches.
The paper tackles the problem of improving the accuracy-interpretability trade-off in machine learning decision trees by using Maximum Satisfiability technology to compute Minimum Pure Decision Trees, resulting in faster runtime and higher accuracy compared to sklearn-generated trees.
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.