CART: Classification using CART

In fdm2id: Data Mining and R Programming for Beginners

Classification using CART

Description

This function builds a classification model using CART.

Usage

CART(
  train,
  labels,
  minsplit = 1,
  maxdepth = log2(length(labels)),
  cp = NULL,
  xval = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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.
xval	The number of cross-validation folds used to choose cp, when cp is NULL. xval = nrow (train) gives a leave-one-out cross-validation, which fits one tree per observation and costs about nrow (train) / 10 times as much.
tune	If true, the function returns parameters instead of a classification model.
methodparameters	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: CART does not support reusing pre-tuned parameters.
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: CART does not produce a plot.
seed	A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.
...	Other parameters.

Value

The classification model.

See Also

cartdepth, cartinfo, cartleafs, cartnodes, cartplot, rpart

Examples

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

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.