RFpredInterval-package {RFpredInterval}

RFpredInterval: A package for building prediction intervals with random forests and boosted forests

Description

RFpredInterval provides methods to build prediction intervals with random forests. The methods provided in the package are Prediction Intervals with Boosted Forests (PIBF) proposed by Alakus et al. (2022) and 15 distinct variations to build PIs proposed by Roy and Larocque (2020). RFpredInterval includes two main functions: pibf() and rfpi(). pibf() applies the PIBF method and it uses the ranger package (Wright and Ziegler, 2017) to fit random forests. rfpi() applies the 15 variations proposed by Roy and Larocque (2020). For rfpi(), RFpredInterval uses randomForestSRC package. For the least-squares splitting rule, both randomForestSRC and ranger packages are applicable.

Details

Among 16 methods, ten of them have specialized splitting rules in the random forest growing process. These methods are the ones with L1 and shortest prediction interval (SPI) splitting rules proposed by Roy and Larocque (2020). To implement these methods, the custom split feature of the randomForestSRC package (Ishwaran and Kogalur, 2021) have been utilised.

The randomForestSRC package allows users to define a custom splitting rule for the tree growing process. The user needs to define the customized splitting rule in the splitCustom.c file. After modifying the splitCustom.c file, all C source code files under the src folder of the package must be recompiled. Finally, the package must be re-installed for the custom split rule to become available. RFpredInterval uses randomForestSRC package by freezing at the version 2.11.0.

RFpredInterval functions

pibf rfpi piall plot.rfpredinterval print.rfpredinterval

References

Alakus, C., Larocque, D., & Labbe, A. (2022). RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests. R JOURNAL, 14(1), 300-319.

Ishwaran H, Kogalur U (2021). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 2.11.0, https://cran.r-project.org/package=randomForestSRC.

Roy, M. H., & Larocque, D. (2020). Prediction intervals with random forests. Statistical methods in medical research, 29(1), 205-229. doi:10.1177/0962280219829885.

Wright MN, Ziegler A (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1–17. doi:10.18637/jss.v077.i01.