# RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

arXiv: https://arxiv.org/abs/2106.08217 · HTML: https://mlsanity.com/paper/2106.08217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate](https://mlsanity.com/paper/1803.08000/) (2018)
- [A Unified Framework for Random Forest Prediction Error Estimation](https://mlsanity.com/paper/1912.07435/) (2019)
- [Conditional canonical correlation estimation based on covariates with random forests](https://mlsanity.com/paper/2011.11555/) (2020)

## Built on by (most cited first, 1 of 1)

- [Covariance regression with random forests](https://mlsanity.com/paper/2209.08173/) (2022)

## Abstract (arXiv)

Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the prediction. Prediction intervals provide information about the reliability of the point predictions. We have developed a comprehensive R package, RFpredInterval, that integrates 16 methods to build prediction intervals with random forests and boosted forests. The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020). We perform an extensive simulation study and apply real data analyses to compare the performance of the proposed method to ten existing methods for building prediction intervals with random forests. The results show that the proposed method is very competitive and, globally, outperforms competing methods.