Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives

ArXi:2506.20114v5 Announce Type: replace-cross Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response.