Interpretable decision trees through MaxSAT
Josep Alòs, Carlos Ansótegui, Eduard Torres
Abstract
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 .
Topics & Concepts
InterpretabilityComputer scienceDecision treeMaximum satisfiability problemArtificial intelligenceMachine learningSatisfiabilityAlgorithmBoolean functionExplainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningMachine Learning and Data Classification