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)是一种相对新的统计学习方法,其目的是在预测准确性和解释性之间求得平衡。从决策树集合,如树加注或随机森林,PREs保留了最后预测模型中的少量树节点。这些节点可以写成形式简单规则,如果[条件]和[准备]的话。结果,PRE往往比完全决策树集合要复杂得多,但发现在许多情况下,它们提供了类似的预测准确性。目前的文件介绍了方法,并展示了如何通过心理研究的一些真实数据实例,使用R包前的包件来适应PREs。这些例子还说明了包件\ textbf{pre}的一些特征,这些特征可能对心理学应用特别有用:支持绝对性、多变和计数反应、应用(非)强化性制约、纳入确认性规则和标准化不同重要性措施。