稀疏决策树的SORTeD Rashomon集：随时枚举
SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration
AI总结：
本文提出SORTD框架，针对稀疏决策树Rashomon集枚举的NP难问题，通过按目标值顺序枚举实现随时行为，实验显示运行时间比现有方法降低最多两个数量级，并支持可分离全序目标及偏序目标的后评估。
摘要：
稀疏决策树学习通过在（软）规模限制内找到单一最准确的树，提供准确且可解释的预测模型，非常适合高风险应用。与其依赖单一“最佳”树，Rashomon集——性能相似但结构不同的树——可用于增强变量重要性分析、丰富解释，并让用户选择更简单的树或满足利益相关者偏好（如公平性）的树，而无需将这些标准硬编码到目标函数中。然而，由于寻找最优树是NP难问题，枚举Rashomon集本身就具有挑战性。因此，我们引入了SORTD，这是一个新颖的框架，它提高了可扩展性，并按目标值顺序枚举Rashomon集中的树，从而提供随时行为。我们的实验表明，与最先进的方法相比，SORTD将运行时间减少了最多两个数量级。此外，SORTD可以计算任何可分离且全序目标的Rashomon集，并支持使用其他可分离（且偏序）目标对集合进行后评估。这些进展共同使得在现实世界应用中探索Rashomon集更加实用。
英文摘要：
Sparse decision tree learning provides accurate and interpretable predictive models that are ideal for high-stakes applications by finding the single most accurate tree within a (soft) size limit. Rather than relying on a single "best" tree, Rashomon sets-trees with similar performance but varying structures-can be used to enhance variable importance analysis, enrich explanations, and enable users to choose simpler trees or those that satisfy stakeholder preferences (e.g., fairness) without hard-coding such criteria into the objective function. However, because finding the optimal tree is NP-hard, enumerating the Rashomon set is inherently challenging. Therefore, we introduce SORTD, a novel framework that improves scalability and enumerates trees in the Rashomon set in order of the objective value, thus offering anytime behavior. Our experiments show that SORTD reduces runtime by up to two orders of magnitude compared with the state of the art. Moreover, SORTD can compute Rashomon sets for any separable and totally ordered objective and supports post-evaluating the set using other separable (and partially ordered) objectives. Together, these advances make exploring Rashomon sets more practical in real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
