MachineShop (version 2.0.0)

TreeModel: Classification and Regression Tree Models

Description

A tree is grown by binary recursive partitioning using the response in the specified formula and choosing splits from the terms of the right-hand-side.

Usage

TreeModel(
  mincut = 5,
  minsize = 10,
  mindev = 0.01,
  split = c("deviance", "gini")
)

Arguments

mincut

minimum number of observations to include in either child node.

minsize

smallest allowed node size: a weighted quantity.

mindev

within-node deviance must be at least this times that of the root node for the node to be split.

split

splitting criterion to use.

Value

MLModel class object.

Details

Response Types:

factor, numeric

Further model details can be found in the source link below.

See Also

tree, fit, resample

Examples

# NOT RUN {
fit(Species ~ ., data = iris, model = TreeModel)

# }