Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then [prediction]. As a result, PREs are often much less complex than full decision tree ensembles, while they have been found to provide similar predictive performance in many situations. The current article introduces the methodology and shows how PREs can be fitted using the R package pre through several real-data examples from psychological research. The examples also illustrate a number of features of package pre that may be particularly useful for applications in psychology: support for categorical, multivariate and count responses, application of (non)negativity constraints, inclusion of confirmatory rules and standardized variable importance measures. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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使预测规则适合心理学研究数据：简介和教程。

预测规则集成（PRE）是一种相对较新的统计学习方法，旨在在预测性​​能和可解释性之间取得平衡。从决策树集合（如增强树集合或随机森林）开始，PRE在最终预测模型中保留一小部分树节点。如果[条件]则[预测]，可以将这些节点写为以下形式的简单规则。结果，PRE通常比完整的决策树集成要简单得多，尽管发现它们在许多情况下都提供类似的预测性能。当前文章介绍了该方法，并通过心理学研究中的几个实际数据示例展示了如何使用R包pre来拟合PRE。这些示例还说明了pre pre的许多功能，这些功能可能对心理学中的应用特别有用：支持分类，多元和计数响应，应用（非）负性约束，包含确认性规则和标准化的变量重要性度量。（PsycINFO数据库记录（c）2020 APA，保留所有权利）。