# Fitting Prediction Rule Ensembles with R Package pre

arXiv: https://arxiv.org/abs/1707.07149 · HTML: https://mlsanity.com/paper/1707.07149/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation](https://mlsanity.com/paper/2310.03112/) (2023)
- [Extending Explainable Ensemble Trees (E2Tree) to regression contexts](https://mlsanity.com/paper/2409.06439/) (2024)
- [Improved prediction rule ensembling through model-based data generation](https://mlsanity.com/paper/2109.13672/) (2021)
- [Causal rule ensemble approach for multi-arm data](https://mlsanity.com/paper/2504.17166/) (2025)
- [SplitWise Regression: Stepwise Modeling with Adaptive Dummy Encoding](https://mlsanity.com/paper/2505.15423/) (2025)

## Abstract (arXiv)

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.