Grow two random forests on two cross-validation folds.
Instead of out-of-bag data, the other fold is used to compute permutation importance.
Related to the novel permutation variable importance by Janitza et al. (2015).
Usage
holdoutRF(formula, data, ...)
Arguments
formula
Object of class formula or character describing the model to fit.
data
Training data of class data.frame, matrix or gwaa.data (GenABEL).
...
Further arguments passed to ranger().
Value
Hold-out random forests with variable importance.
References
Janitza, S., Celik, E. & Boulesteix, A.-L., (2015). A computationally fast variable importance test for random forest for high dimensional data, Technical Report 185, University of Munich, https://epub.ub.uni-muenchen.de/25587.