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

## TL;DR

This paper introduces prediction rule ensembles (PREs) as an interpretable and accurate statistical learning method, demonstrating their application to psychological research data using the R package pre.

## Contribution

It provides a comprehensive tutorial on fitting PREs, highlighting their features and advantages for psychological data analysis.

## Key findings

- PREs achieve similar accuracy to full ensemble models with simpler rules
- The R package pre supports diverse data types and constraints for psychological research
- PREs offer interpretable models with standardized variable importance measures

## Abstract

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.