Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive accuracy 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 accuracy in many situations. The current paper 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 \textbf{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.

翻译：预测规则集合（PREs）是一种相对较新的统计学习方法，旨在平衡预测准确性与可解释性。该方法从决策树集成（如提升树集成或随机森林）出发，仅保留最终预测模型中少量树节点。这些节点可转化为形如"若[条件]则[预测]"的简单规则。因此，与完整决策树集成相比，PREs通常复杂度显著降低，同时在许多情境下能保持相近的预测精度。本文介绍了该方法的原理，并通过心理学研究的若干真实数据示例，展示如何利用R语言中的pre包拟合PREs。这些示例还阐述了pre包在心理学应用中特别实用的多项功能：支持分类、多变量及计数型响应变量，应用（非）负性约束，纳入验证性规则以及标准化变量重要性度量。