Hold-out random forests

Description

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

1

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.

Author(s)

Marvin N. Wright

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.

See Also

ranger