Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles

TL;DR

This paper presents a lossless pruning method for boosting tree ensembles that reduces their size without affecting predictive performance, enhancing interpretability and inference efficiency.

Contribution

We introduce a novel, exact optimization-based pruning technique that guarantees functional equivalence, enabling significant size reduction of large ensembles without loss.

Findings

Significantly reduces ensemble size while preserving accuracy

Pruning is lossless for any aggregated metric

Method is fast and effective for large ensembles

Abstract

Tree ensembles, including boosting methods, are highly effective and widely used for tabular data. However, large ensembles lack interpretability and require longer inference times. We introduce a method to prune a tree ensemble into a reduced version that is "functionally identical" to the original model. In other words, our method guarantees that the prediction function stays unchanged for any possible input. As a consequence, this pruning algorithm is lossless for any aggregated metric. We formalize the problem of functionally identical pruning on ensembles, introduce an exact optimization model, and provide a fast yet highly effective method to prune large ensembles. Our algorithm iteratively prunes considering a finite set of points, which is incrementally augmented using an adversarial model. In multiple computational experiments, we show that our approach is a "free lunch", significantly reducing the ensemble size without altering the model's behavior. Thus, we can preserve state-of-the-art performance at a fraction of the original model's size.