Decision tree boosted varying coefficient models
Decision tree boosted varying coefficient models
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DOI：
10.1007/s10618-022-00863-y
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发表时间：
2022-09
影响因子：
4.8
通讯作者：
Yichen Zhou;G. Hooker
中科院分区：
文献类型：
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作者：
Yichen Zhou;G. Hooker
变系数模型是通用参数模型的灵活扩展，其系数是一组效应修改协变量的函数，而不是拟合常数。它们能够实现更高的模型复杂性，同时保留底层参数模型的结构，从而生成可解释的预测。在本文中，我们研究使用梯度提升决策树作为这些系数决定功能的变系数模型与线性结构的输出。与传统的样条曲线或核平滑器的选择相比，提升树更灵活，因为它们在效果修改器空间中不需要结构假设。我们介绍了我们提出的方法从本地化版本的梯度下降的角度来看，证明其理论上的一致性，在温和的假设下通常适应决策树研究，并实证证明，所提出的树提升变系数模型实现高性能合格的训练速度，预测精度和可理解性相比，几个基准算法。
Varying coefficient models are a flexible extension of generic parametric models whose coefficients are functions of a set of effect-modifying covariates instead of fitted constants. They are capable of achieving higher model complexity while preserving the structure of the underlying parametric models, hence generating interpretable predictions. In this paper we study the use of gradient boosted decision trees as those coefficient-deciding functions in varying coefficient models with linearly structured outputs. In contrast to the traditional choices of splines or kernel smoothers, boosted trees are more flexible since they require no structural assumptions in the effect modifier space. We introduce our proposed method from the perspective of a localized version of gradient descent, prove its theoretical consistency under mild assumptions commonly adapted by decision tree research, and empirically demonstrate that the proposed tree boosted varying coefficient models achieve high performance qualified by their training speed, prediction accuracy and intelligibility as compared to several benchmark algorithms.