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.

翻译：我们提出了一个改进机器学习(ML)决策树(DTs)的准确性和可解释性权衡的方法,特别是我们运用最大满足性技术来计算最低纯度DTs(MPDTs ) 。 我们改进了以往方法的运行时间,并表明这些MPDs能够超过ML框架所生成的DTs的准确性。