Description Usage Arguments Details Value See Also Examples
View source: R/ML_GAMBoostModel.R
Gradient boosting for optimizing arbitrary loss functions, where componentwise arbitrary baselearners, e.g., smoothing procedures, are utilized as additive baselearners.
1 2 3 4 5 6 7 8 9 10 
family 
optional 
baselearner 
character specifying the componentwise

dfbase 
gobal degrees of freedom for Pspline base learners
( 
mstop 
number of initial boosting iterations. 
nu 
step size or shrinkage parameter between 0 and 1. 
risk 
method to use in computing the empirical risk for each boosting iteration. 
stopintern 
logical inidicating whether the boosting algorithm stops internally when the outofbag risk increases at a subsequent iteration. 
trace 
logical indicating whether status information is printed during the fitting process. 
binary factor
, BinomialVariate
,
NegBinomialVariate
, numeric
, PoissonVariate
,
Surv
mstop
Default values for the NULL
arguments and further model details can be
found in the source links below.
MLModel
class object.
gamboost
, Family
,
baselearners
, fit
,
resample
1 2 3 4 5  ## Requires prior installation of suggested package mboost to run
data(Pima.tr, package = "MASS")
fit(type ~ ., data = Pima.tr, model = GAMBoostModel)

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