CART {fdm2id}

Classification using CART

Description

This function builds a classification model using CART.

Usage

CART(
  train,
  labels,
  minsplit = 1,
  maxdepth = log2(length(labels)),
  cp = NULL,
  tune = FALSE,
  ...
)

Arguments

train

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

labels

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

minsplit

The minimum leaf size during the learning.

maxdepth

Set the maximum depth of any node of the final tree, with the root node counted as depth 0.

cp

The complexity parameter of the tree. Cross-validation is used to determine optimal cp if NULL.

tune

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

...

Other parameters.

Value

The classification model.

See Also

cartdepth, cartinfo, cartleafs, cartnodes, cartplot, rpart

Examples

require (datasets)
data (iris)
CART (iris [, -5], iris [, 5])