rfLSF {caretLSF} | R Documentation |
RandomForest model building in parallel using LSF
rfLSF(x, y, workers = 10, control = lsf.ctrl(), ...)
x |
a data frame or a matrix of predictor |
y |
A response vector. If a factor, classification is assumed, otherwise regression is assumed. If omitted, randomForest will run in unsupervised mode. |
workers |
the number of compute nodes. Note that the value of ntree will be used for each compute node. |
control |
an optional control object for starting parallel jobs |
... |
various options to pass to randomForest.default |
The function calls randomForest.default
on several nodes and uses combine
put all of the models back together.
Note that the call of the output object will mirror the values of x
and y
passed to rfLSF
.
a randomForest
object
Max Kuhn
~~objects to See Also as help
, ~~~
## Not run: ## Classification: ##data(iris) set.seed(71) iris.rf <- rfLSF( iris[, 1:4], iris$Species, importance=TRUE, proximity=TRUE) ## End(Not run)