| tuneRF {randomForest} | R Documentation |
Starting with the default value of mtry, search for the optimal value (with respect to Out-of-Bag error estimate) of mtry for randomForest.
tuneRF(x, y, mtryStart, ntreeTry=50, stepFactor=2, improve=0.05,
trace=TRUE, plot=TRUE, doBest=FALSE, ...)
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 |
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.
data(fgl, package="MASS") fgl.res <- tuneRF(fgl[,-10], fgl[,10], stepFactor=1.5)