rfinterval: Prediction Intervals for Random forests

In rfinterval: Predictive Inference for Random Forests

Description

The rfinterval constructs prediction intervals for random forest predictions using a fast implementation package 'ranger'.

Usage

rfinterval(formula = NULL, train_data = NULL, test_data = NULL,
  method = c("oob", "split-conformal", "quantreg"), alpha = 0.1,
  symmetry = TRUE, seed = NULL, params_ranger = NULL)

Arguments

formula	Object of class formula or character describing the model to fit. Interaction terms supported only for numerical variables.
train_data	Training data of class data.frame, matrix, or dgCMatrix (Matrix).
test_data	Test data of class data.frame, matrix, or dgCMatrix (Matrix).
method	Method for constructing prediction interval. If method = "oob", compute the out-of-bag prediction intervals; if method = "split-conformal", compute the split conformal prediction interval; if method = "quantreg", use quantile regression forest to compute prediction intervals.
alpha	Confidence level. alpha = 0.05 for the 95% prediction interval.
symmetry	True if constructing symmetric out-of-bag prediction intervals, False otherwise. Only for method = "oob"
seed	Seed (only for method = "split-conformal")
params_ranger	List of further parameters that should be passed to ranger. See ranger for possible parameters.

Value

oob_interval	Out-of-bag prediction intervals
sc_interval	Split-conformal prediction intervals
quantreg_interval	Quantile regression forest prediction intervals
alpha	Confidence level for prediction intervals
testPred	Random forest prediction for test set
train_data	Training data
test_data	Test data

References

Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Dan Nordman. (2019). "Random Forest Prediction Intervals." The American Statistician. Doi: 10.1080/00031305.2019.1585288.

Haozhe Zhang. (2019). "Topics in Functional Data Analysis and Machine Learning Predictive Inference." Ph.D. Dissertations. Iowa State University Digital Repository. 17929.

Lei, J., Max G’Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and Larry Wasserman. "Distribution-free predictive inference for regression." Journal of the American Statistical Association 113, no. 523 (2018): 1094-1111.

Meinshausen, Nicolai. "Quantile regression forests." Journal of Machine Learning Research 7 (2006): 983-999.

Leo Breiman. (2001). Random Forests. Machine Learning 45(1), 5-32.

Examples

train_data <- sim_data(n = 500, p = 8)
test_data <- sim_data(n = 500, p = 8)
output <- rfinterval(y~., train_data = train_data, test_data = test_data,
                     method = c("oob", "split-conformal", "quantreg"),
                     symmetry = TRUE,alpha = 0.1)
y <- test_data$y
mean(output$oob_interval$lo < y & output$oob_interval$up > y)
mean(output$sc_interval$lo < y & output$sc_interval$up > y)
mean(output$quantreg_interval$lo < y & output$quantreg_interval$up > y)

rfinterval documentation built on July 18, 2019, 5:03 p.m.