RANDOMFOREST: Classification using Random Forest

In fdm2id: Data Mining and R Programming for Beginners

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,
  ...
)

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.

Value

The classification model.

See Also

randomForest

Examples

## Not run: 
require (datasets)
data (iris)
RANDOMFOREST (iris [, -5], iris [, 5])

## End(Not run)

fdm2id documentation built on July 9, 2023, 6:05 p.m.