Fitting Prediction Rule Ensembles with R Package pre

TL;DR

The paper introduces the R package pre for fitting prediction rule ensembles, demonstrating its effectiveness and interpretability in comparison with other models on benchmark datasets.

Contribution

The paper presents the implementation of the pre package for deriving prediction rule ensembles, highlighting its accuracy and sparsity advantages over traditional models.

Findings

pre achieves accuracy comparable to random forests

pre produces sparser models with fewer variables

Application on depression dataset illustrates interpretability

Abstract

Prediction rule ensembles (PREs) are sparse collections of rules, offering highly interpretable regression and classification models. This paper presents the R package pre, which derives PREs through the methodology of Friedman and Popescu (2008). The implementation and functionality of package pre is described and illustrated through application on a dataset on the prediction of depression. Furthermore, accuracy and sparsity of PREs is compared with that of single trees, random forest and lasso regression in four benchmark datasets. Results indicate that pre derives ensembles with predictive accuracy comparable to that of random forests, while using a smaller number of variables for prediction.