rfinterval: Predictive Inference on Random Forests (beta)

Haozhe Zhang

Introduction

rfinterval is a fast implementation of random forest prediction intervals described in the paper "Random Forest Prediction Intervals" by Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman. This R package produce out-of-bag prediction intervals, split conformal prediction intervals, and quantile regression forest intervals for random forest predictions.

Installation

To install the R package rfinterval directly from github, run the following in R:

#install.packages("devtools")
#devtools::install_github(repo="haozhestat/rfinterval")
install.packages("rfinterval")
library(rfinterval)
?rfinterval

Usage

Quickstart:

train_data <- sim_data(n = 1000, p = 10)
test_data <- sim_data(n = 1000, p = 10)

output <- rfinterval(y~., train_data = train_data, test_data = test_data,
                     method = c("oob", "split-conformal", "quantreg"),
                     symmetry = TRUE,alpha = 0.1)
                     
### print the marginal coverage of OOB prediction interval
mean(output$oob_interval$lo < test_data$y & output$oob_interval$up > test_data$y)

### print the marginal coverage of Split-conformal prediction interval
mean(output$sc_interval$lo < test_data$y & output$sc_interval$up > test_data$y)

### print the marginal coverage of Quantile regression forest prediction interval
mean(output$quantreg_interval$lo < test_data$y & output$quantreg_interval$up > test_data$y)

Data example:

oob_interval <- rfinterval(pm2.5 ~ .,
                            train_data = BeijingPM25[1:1000, ],
                            test_data = BeijingPM25[1001:2000, ],
                            method = "oob",
                            symmetry = TRUE,
                            alpha = 0.1)
str(oob_interval)

If you find any bugs, or if you experience any crashes, please report to us. If you have any questions just ask, we won't bite.

Please cite our paper if you use rfinterval.

References

• Zhang, H., Zimmerman, J., Nettleton, D. and Nordman, D. (2019). "Random Forest Prediction Intervals." The American Statistician, doi: 10.1080/00031305.2019.1585288.
• Zhang, H. (2019) "Topics in Functional Data Analysis and Machine Learning Predictive Inference." Graduate Theses and Dissertations.
• Lei, J., Max G’Sell, A. R., Tibshirani, R.J., and Wasserman, L. "Distribution-free predictive inference for regression." Journal of the American Statistical Association 113, no. 523 (2018): 1094-1111.
• Meinshausen, N. "Quantile regression forests." Journal of Machine Learning Research 7, no. Jun (2006): 983-999.
• Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H. and Chen, S.X., 2015. "Assessing Beijing's PM2. 5 pollution: severity, weather impact, APEC and winter heating". Proc. R. Soc. A, 471(2182), p.20150257.