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

TL;DR

RFpredInterval is an R package that offers 16 methods, including a new approach, for constructing prediction intervals with random and boosted forests, demonstrating superior performance through extensive simulations and real data applications.

Contribution

The paper introduces RFpredInterval, a comprehensive R package with a novel method for prediction intervals using boosted forests, outperforming existing methods.

Findings

The new PIBF method is highly competitive in prediction interval accuracy.

RFpredInterval outperforms ten existing methods in simulations.

The package provides a unified framework for prediction intervals with forests.

Abstract

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.