Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

Interpretability in machine learning is the ability to explain or to present in understandable terms to a human [8]. Interpretability is particularly important when, for example the goal of the user is to gain knowledge from some form of explanations about the data or process through machine learning models, or when making high-stakes decisions based on the outputs from the machine learning models where the user has to be able to trust the models. In this work we address the problem of explaining and understanding tree-ensemble learners by extracting meaningful rules from them. This problem is of practical relevance in business domains where the understanding of the behavior of high-performing machine learning models and extraction of knowledge in human readable form can aid users in the decision making process. We use Answer Set Programming (ASP) [14, 22] to generate rule sets from tree-ensembles.