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,
  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])

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