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

Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees