  Tree ensemble methods represent a popular machine learning model, known for
their effectiveness in supervised classification and regression tasks. Their
performance derives from aggregating predictions of multiple decision trees,
which are renowned for their interpretability properties. However, tree
ensemble methods do not reliably exhibit interpretable output. Our work aims to
extract an optimized list of rules from a trained tree ensemble, providing the
user with a condensed, interpretable model that retains most of the predictive
power of the full model. Our approach consists of solving a clean and neat set
partitioning problem formulated through Integer Programming. The proposed
method works with either tabular or time series data, for both classification
and regression tasks, and does not require parameter tuning under the most
common setting. Through rigorous computational experiments, we offer
statistically significant evidence that our method is competitive with other
rule extraction methods and effectively handles time series.
