tuneRF: Tune randomForest for the optimal mtry parameter

In randomForest: Breiman and Cutlers Random Forests for Classification and Regression

Tune randomForest 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 randomForest.

Usage

tuneRF(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 randomForest
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 randomForest

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 randomForest object produced with the optimal mtry.

See Also

randomForest

Examples

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

randomForest documentation built on Sept. 30, 2024, 9:19 a.m.