Description Usage Arguments Details Value See Also Examples
View source: R/ML_AdaBoostModel.R
Fits the AdaBoost.M1 (Freund and Schapire, 1996) and SAMME (Zhu et al., 2009) algorithms using classification trees as single classifiers.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  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
)

boos 
if 
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 crossvalidations. 
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. 
factor
mfinal
, maxdepth
, coeflearn
*
* excluded from grids by default
Further model details can be found in the source link below.
MLModel
class object.
1 2 3  ## Requires prior installation of suggested package adabag to run
fit(Species ~ ., data = iris, model = AdaBoostModel(mfinal = 5))

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