fdm2id (version 0.9.6)

RANDOMFOREST: Classification using Random Forest

Description

This function builds a classification model using Random Forest

Usage

RANDOMFOREST(
  train,
  labels,
  ntree = 500,
  nvar = if (!is.null(labels) && !is.factor(labels)) max(floor(ncol(train)/3), 1) else
    floor(sqrt(ncol(train))),
  tune = FALSE,
  ...
)

Value

The classification model.

Arguments

train

The training set (description), as a data.frame.

labels

Class labels of the training set (vector or factor).

ntree

The number of trees in the forest.

nvar

Number of variables randomly sampled as candidates at each split.

tune

If true, the function returns paramters instead of a classification model.

...

Other parameters.

See Also

randomForest

Examples

if (FALSE) {
require (datasets)
data (iris)
RANDOMFOREST (iris [, -5], iris [, 5])
}