View source: R/ML_GAMBoostModel.R
GAMBoostModel  R Documentation 
Gradient boosting for optimizing arbitrary loss functions, where componentwise arbitrary baselearners, e.g., smoothing procedures, are utilized as additive baselearners.
GAMBoostModel( family = NULL, baselearner = c("bbs", "bols", "btree", "bss", "bns"), dfbase = 4, mstop = 100, nu = 0.1, risk = c("inbag", "oobag", "none"), stopintern = FALSE, trace = FALSE )
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 and further model details can be found in the source links below.
MLModel
class object.
gamboost
, Family
,
baselearners
, fit
,
resample
## 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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