RPartModel: Recursive Partitioning and Regression Tree Models

In MachineShop: Machine Learning Models and Tools

Recursive Partitioning and Regression Tree Models

Description

Fit an rpart model.

Usage

RPartModel(
  minsplit = 20,
  minbucket = round(minsplit/3),
  cp = 0.01,
  maxcompete = 4,
  maxsurrogate = 5,
  usesurrogate = 2,
  xval = 10,
  surrogatestyle = 0,
  maxdepth = 30
)

Arguments

minsplit	minimum number of observations that must exist in a node in order for a split to be attempted.
minbucket	minimum number of observations in any terminal node.
cp	complexity parameter.
maxcompete	number of competitor splits retained in the output.
maxsurrogate	number of surrogate splits retained in the output.
usesurrogate	how to use surrogates in the splitting process.
xval	number of cross-validations.
surrogatestyle	controls the selection of a best surrogate.
maxdepth	maximum depth of any node of the final tree, with the root node counted as depth 0.

Details

Response types:

factor, numeric, Surv

Automatic tuning of grid parameter:

cp

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

Value

MLModel class object.

See Also

rpart, fit, resample

Examples

## Requires prior installation of suggested packages rpart and partykit to run

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

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.