提出统一框架评估决策树搜索策略，显著提升分类与回归效率
Search Strategies for Optimal Classification and Regression Trees
- 构建通用算法框架，整合并可扩展多种搜索策略
- 分类任务性能超越现有最优，回归任务加速超10倍
- 适合关注可解释模型与高效算法的科研与工程人员
最优决策树（ODTs）是紧凑且可解释的机器学习模型，能全局优化目标函数，但其可扩展性仍具挑战。尽管近期研究提出了多种搜索策略以提升可扩展性，但各策略的具体贡献尚不清晰。为此，本文提出一个通用的ODT算法框架，可实例化已有策略并支持新策略定义。该框架提供统一视角，用于理解和比较不同策略，我们据此对18种搜索策略进行了实证分析。相比当前最优方法，本评估中表现最佳的策略在分类任务上实现显著更好的即时性能，在回归任务上将运行时间缩短超过一个数量级。
原文摘要 · Abstract (English)
Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging. While recent work has proposed a variety of search strategies to improve scalability, the precise contribution of each strategy remains unclear. To address this gap, we introduce a general algorithmic framework for ODTs that instantiates previously used search strategies and enables the definition of new ones. This provides a common lens through which to understand and compare different strategies, which we use to empirically investigate the effect of 18 search strategies. Compared to the state of the art, the best strategy in our evaluation achieves significantly better anytime performance for classification, and improves runtime by more than an order of magnitude for regression.
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