fdm2id (version 1.0.1)

CART: 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,
  ...
)

Value

The classification model.

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.

See Also

cartdepth, cartinfo, cartleafs, cartnodes, cartplot, rpart

Examples

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