tuneRRF: Tune RRF for the optimal mtry parameter

In RRF: Regularized Random Forest

Tune RRF for the optimal mtry parameter

Description

Starting with the default value of mtry, search for the optimal value (with respect to Out-of-Bag error estimate) of mtry for RRF.

Usage

tuneRRF(x, y, mtryStart, ntreeTry=50, stepFactor=2, improve=0.05,
       trace=TRUE, plot=TRUE, doBest=FALSE, ...)

Arguments

x	matrix or data frame of predictor variables
y	response vector (factor for classification, numeric for regression)
mtryStart	starting value of mtry; default is the same as in RRF
ntreeTry	number of trees used at the tuning step
stepFactor	at each iteration, mtry is inflated (or deflated) by this value
improve	the (relative) improvement in OOB error must be by this much for the search to continue
trace	whether to print the progress of the search
plot	whether to plot the OOB error as function of mtry
doBest	whether to run a forest using the optimal mtry found
...	options to be given to RRF

Value

If doBest=FALSE (default), it returns a matrix whose first column contains the mtry values searched, and the second column the corresponding OOB error.

If doBest=TRUE, it returns the RRF object produced with the optimal mtry.

See Also

RRF

Examples

data(fgl, package="MASS")
fgl.res <- tuneRRF(fgl[,-10], fgl[,10], stepFactor=1.5)

RRF documentation built on May 31, 2022, 1:05 a.m.