AdaBoostModel: 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
)

Value

MLModel class object.

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.

Examples

Run this code

# \donttest{
## Requires prior installation of suggested package adabag to run

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