MachineShop: TreeModel – R documentation

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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"),
  k = NULL,
  best = NULL,
  method = c("deviance", "misclass")
)

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.
`k`	scalar cost-complexity parameter defining a subtree to return.
`best`	integer alternative to `k` requesting the number of terminal nodes of a subtree in the cost-complexity sequence to return.
`method`	character string denoting the measure of node heterogeneity used to guide cost-complexity pruning.

Details

Response Types:: factor, numeric

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

Value

MLModel class object.

Examples

## Requires prior installation of suggested package tree to run

fit(Species ~ ., data = iris, model = TreeModel)

MachineShop

Machine Learning Models and Tools

v2.8.0

GPL-3

Authors

Brian J Smith [aut, cre]

Initial release

2021-04-16