Prediction Intervals with Random Forests and Boosted Forests

Implements various prediction interval methods with random forests and boosted forests. The package has two main functions: pibf() produces prediction intervals with boosted forests (PIBF) as described in Alakus et al. (2022) and rfpi() builds 15 distinct variations of prediction intervals with random forests (RFPI) proposed by Roy and Larocque (2020) .

RFpredInterval

R package which implements Prediction Intervals with Random Forests and Boosted Forests

RFpredInterval package has two main R functions as below:

• pibf: Constructs prediction intervals with Prediction Intervals with Boosted Forests (PIBF) proposed by Alakus et al. (2021)
• rfpi: Constructs prediction intervals with 15 distinct variations to produce Prediction Intervals with Random Forests (RFPI) proposed by Roy and Larocque (2020)

Installation

This package is available on CRAN. Alternatively, you can install RFpredInterval from GitHub using the devtools package. Run the following code in R to install:

if (!require(devtools)) {
  install.packages("devtools")
  library(devtools)
}
devtools::install_github('calakus/RFpredInterval')

References

• Alakus, C., Larocque, D., and Labbe, A. (2021). RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests. arXiv preprint https://arxiv.org/abs/2106.08217.
• M.-H. Roy and D. Larocque. Prediction intervals with random forests. Statistical Methods in Medical Research, 29(1):205–229, Jan. 2020. URL https://doi.org/10.1177/0962280219829885.