ArborEnum: Decision Tree Rashomon Sets over Continuous Features
The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability. These use cases motivate the computation of Rashomon sets: the set of all models whose regularized loss is near-optimal. Decision trees are one of the few model classes for which Rashomon sets can be fully enumerated, but this computation has always been conditional on a binarization of the original data, either restricting which splits each tree is allowed to make or substantially increasing the complexity of an already difficult combinatorial problem. We introduce the fir...
| Authors | Zakk Heile, Hayden McTavish, Margo Seltzer et al. |
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| Primary category | cs.LG |
| Published | 5 August 2026 |
| Last updated at source | 8 September 2026 |
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