AdaBoostModel: Boosting with Classification Trees

In MachineShop: Machine Learning Models and Tools

Boosting with Classification Trees

Description

Fits the AdaBoost.M1 (Freund and Schapire, 1996) and SAMME (Zhu et al., 2009) algorithms using classification trees as single classifiers.

Usage

AdaBoostModel(
  boos = TRUE,
  mfinal = 100,
  coeflearn = c("Breiman", "Freund", "Zhu"),
  minsplit = 20,
  minbucket = round(minsplit/3),
  cp = 0.01,
  maxcompete = 4,
  maxsurrogate = 5,
  usesurrogate = 2,
  xval = 10,
  surrogatestyle = 0,
  maxdepth = 30
)

Arguments

boos	if TRUE, then bootstrap samples are drawn from the training set using the observation weights at each iteration. If FALSE, then all observations are used with their weights.
mfinal	number of iterations for which boosting is run.
coeflearn	learning algorithm.
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, coeflearn*

* excluded from grids by default

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

Value

MLModel class object.

See Also

boosting, fit, resample

Examples

## Requires prior installation of suggested package adabag to run

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

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