MachineShop (version 3.3.0)

RPartModel: 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.

Value

MLModel class object.

Details

Response types:

factor, numeric, Surv

Automatic tuning of grid parameter:

cp

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

See Also

rpart, fit, resample

Examples

# NOT RUN {
## Requires prior installation of suggested packages rpart and partykit to run

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