AdaBagModel: Bagging with Classification Trees

In MachineShop: Machine Learning Models and Tools

Bagging with Classification Trees

Description

Fits the Bagging algorithm proposed by Breiman in 1996 using classification trees as single classifiers.

Usage

AdaBagModel(
  mfinal = 100,
  minsplit = 20,
  minbucket = round(minsplit/3),
  cp = 0.01,
  maxcompete = 4,
  maxsurrogate = 5,
  usesurrogate = 2,
  xval = 10,
  surrogatestyle = 0,
  maxdepth = 30
)

Arguments

mfinal	number of trees to use.
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

Automatic tuning of grid parameters:

mfinal, maxdepth

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

Value

MLModel class object.

See Also

bagging, fit, resample

Examples

## Requires prior installation of suggested package adabag to run

fit(Species ~ ., data = iris, model = AdaBagModel(mfinal = 5))

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