Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important prediction features, (ii) effectively manage sparse data, and (iii) have excellent prediction capabilities. Despite their advantages, they are generally unpopular for decision-making tasks and black-box optimization, which is due to their difficult-to optimize structure and the lack of a reliable uncertainty measure. ENTMOOT is our new framework for integrating (already trained) tree models into larger optimization problems. The contributions of ENTMOOT include: (i) explicitly introducing a reliable uncertainty measure that is compatible with tree models, (ii) solving the larger optimization problems that incorporate these uncertainty aware tree models, (iii) proving that the solutions are globally optimal, i.e. no better solution exists. In particular, we show how the ENTMOOT approach allows a simple integration of tree models into decision-making and black-box optimization, where it proves as a strong competitor to commonly-used frameworks.

翻译：这些树模型(一) 提供了对重要预测特征的洞察力,(二) 有效地管理稀有数据,(三) 具有极好的预测能力。尽管这些模型有其优点,但它们通常不为决策任务和黑盒优化所欢迎,这是因为它们的结构难以优化,而且缺乏可靠的不确定性衡量标准。ENMOOT是将(经过训练的)树模型纳入更大的优化问题的新框架。ENMOOT的贡献包括:(一) 明确采用与树模型兼容的可靠不确定性计量标准,(二) 解决包含这些了解不确定性的树模型的更大的优化问题,(三) 证明解决办法是全球最佳的,即没有更好的解决办法。特别是,我们展示了ENMOOOT方法如何允许将树模型简单地纳入决策和黑盒优化,在那里,它证明它对于常用的框架具有很强的竞争力。