Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.

中文翻译：

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用于调查研究的基于树的机器学习方法。

机器学习领域的预测建模方法已成为跨学科探索和分析各种数据的流行工具。这些方法通常不需要关于所研究关系的函数形式的特定先验知识，并且能够适应结果与其预测变量之间复杂的非线性和非加性相互关系，同时特别关注预测性能。调查研究人员开始采用这种建模观点，以调整或改进数据收集和/或调查管理的各个方面。为了促进这方面的研究，本文 (1) 介绍了著名的基于树的机器学习方法，