PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence

ArXi:2605.28068v1 Announce Type: new Tree ensembles are machine learning models with strong predictive performance and interpretability, and remain widely used for tabular data. Standard pruning methods for tree ensembles typically optimize an accuracy-compression trade-off and may change a subset of predictions, potentially compromising decision consistency. Faithful pruning methods address this issue by preserving prediction equivalence over the entire input space, but this requirement leads to lower compression ratios.