We present a Combinatorial Optimization approach based on Maximum Satisfiability technology to compute Minimum Pure Decision Trees (MPDTs) for the sake of interpretability. We show that our approach outperforms clearly in terms of runtime previous approaches to compute MPDTs. We additionally show that these MPDTs can outperform on average the DT classifiers generated with sklearn in terms of accuracy. Therefore, our approach tackles favourably the challenge of balancing interpretability and accuracy.
翻译:我们提出了一个基于最大可满足性技术的组合优化方法,用于计算最低纯决定树(MPDTs)以便解释性。我们表明,我们的方法在计算MPDTs(MPDTs)的运行前方法方面明显优于以往的运行时间。我们进一步表明,这些MPDTs在准确性方面平均优于以细微的距离生成的DT分类器。因此,我们的方法积极应对了平衡可解释性和准确性的挑战。