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Prediction rule ensembles (PREs) are a relatively new statistical learningmethod, which aim to strike a balance between predictive accuracy andinterpretability. Starting from a decision tree ensemble, like a boosted treeensemble or a random forest, PREs retain a small subset of tree nodes in thefinal predictive model. These nodes can be written as simple rules of the formif [condition] then [prediction]. As a result, PREs are often much less complexthan full decision tree ensembles, while they have been found to providesimilar predictive accuracy in many situations. The current paper introducesthe methodology and shows how PREs can be fitted using the R package prethrough several real-data examples from psychological research. The examplesalso illustrate a number of features of package \textbf{pre} that may beparticularly 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.

Fitting Prediction Rule Ensembles to Psychological Research Data: An Introduction and Tutorial