Additive models, such as produced by gradient boosting, and full interaction models, such as classification and regression trees (CART), are widely used algorithms that have been investigated largely in isolation. We show that these models exist along a spectrum, revealing never-before-known connections between these two approaches. This paper introduces a novel technique called tree-structured boosting for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although tree-structured boosting is designed primarily to provide both the model interpretability and predictive performance needed for high-stake applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

本文介绍了一种新的技术——树结构提升，可以用单一决策树产生等效于分类和回归树或梯度增强桩的模型，并且可以在两种方法之间生成混合模型。此技术不仅为高风险应用（如医学）提供了模型可解释性和预测性能，而且可以优于这两种方法。

树形提升：梯度提升树桩和完整决策树之间的联系