倒下的树：一种可解释的风险优先级排序模型类
Falling Trees: A Model Class for Interpretable Risk Prioritization
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中文总结 AI 辅助
本文提出倒下的树（falling trees）模型，在保持单调风险约束的同时允许树状分支，并通过GRAVITree算法学习，在临床和公开基准上优于或匹敌现有可解释模型，实现可解释性与表达能力的平衡。
中文摘要 AI 辅助
许多现实世界的决策需要优先处理高风险案例，例如临床医生优先处理高风险患者，然后再处理低风险患者。下降规则列表（FRLs）是一种有序的if-then规则，其风险单调递减，为此类任务提供了可解释的框架；然而，其单路径结构导致模型类高度受限。我们引入了倒下的树（falling trees），一种新的可解释模型家族，它在允许树状分支的同时强制执行相同的单调风险约束。我们提出了GRAVITree，一种新颖的带边界的动态规划算法，用于在深度和分支约束下学习倒下的树的Rashomon集。我们的公式可以在规则列表和完整决策树之间进行插值，从而实现用户期望的模型表达能力。在一个新的临床数据集和许多公开的分类基准测试中，倒下的树在性能上匹配或超越了FRLs和其他可解释基线模型，通常为高风险实例产生更稀疏的决策。我们的结果表明，倒下的树在高风险场景中在可解释性、表达能力和风险优先级排序之间取得了实用的平衡。
英文摘要
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 can interpolate between rule lists and full decision trees, enabling user-desired model expressivity. In a new clinical dataset and in many public classification benchmarks, falling trees match or outperform FRLs and other interpretable baselines, often producing more sparse decisions for high-risk instances. Our results show that falling trees strike a practical balance between interpretability, expressiveness, and risk prioritization for high-stakes settings.
发表机构
- Duke University（杜克大学）
- University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
