Falling Trees: A Model Class for Interpretable Risk Prioritization
Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule lists (FRLs), which are ordered if--then rules with monotonically decreasing risks, provide an interpretable framework for such tasks; however, their single-path structure yields a highly restricted model class. We introduce falling trees, a new family of interpretable models that enforces the same monotonic risk constraint while permitting tree-structured branching. We present GRAVITree, a novel dynamic-programming-with-bounds algorithm for learning the Rashomon set of falling trees under depth and branching constraints. Our formulation c...
| Authors | Varun Babbar, Zachery Boner, Margo Seltzer et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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