AI 中文总结
本文针对最优决策树可扩展性研究的空白，提出通用算法框架，实证分析18种搜索策略，找到的最优策略在分类任务上任何时候性能更优，回归任务运行时间提升超一个数量级。
AI 中文摘要
最优决策树（ODTs）是紧凑、可解释的机器学习模型，可全局优化给定目标函数，但可扩展性仍是挑战。近期研究提出多种提升可扩展性的搜索策略，不过各策略的具体贡献尚不明确。为填补该空白，本文提出适用于ODTs的通用算法框架，可实例化已有搜索策略并支持定义新策略，该框架提供了理解与比较不同策略的统一视角，据此对18种搜索策略开展实证研究。与现有最优方法相比，评估中最优策略在分类任务上实现了更优的任何时候性能，在回归任务上将运行时间提升了一个数量级以上。
英文摘要
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.
