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This work introduces the first algorithm that exactly enumerates decision-tree Rashomon sets while exploiting the ordered structure of continuous features, and develops a relaxation for approximate enumeration and an anytime algorithm that progressively refines the set of candidate thresholds, producing increasingly de...
This work introduces Multistage Defer Trees: a sequence of sparse decision trees that each make predictions for most samples, while deferring a small proportion to the next tree in the sequence or, ultimately, to a black box.
It is shown that the learned model weights generalize effectively to MIMIC-IV radiology reports and that the approach achieves near-optimal performance with only a small amount of labeled training data, which makes the approach practical and reproducible for new settings.
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