Gradient Boosting with Additive Models
Gradient boosting for optimizing arbitrary loss functions, where component-wise arbitrary base-learners, e.g., smoothing procedures, are utilized as additive base-learners.
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 component-wise
|
dfbase |
gobal degrees of freedom for P-spline 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 out-of-bag 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.
## 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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