GLMBoostModel {MachineShop} | R Documentation |
Gradient boosting for optimizing arbitrary loss functions where component-wise linear models are utilized as base-learners.
GLMBoostModel( family = NULL, mstop = 100, nu = 0.1, risk = c("inbag", "oobag", "none"), stopintern = FALSE, trace = FALSE )
family |
optional |
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.
glmboost
, Family
,
fit
, resample
## Requires prior installation of suggested package mboost to run data(Pima.tr, package = "MASS") fit(type ~ ., data = Pima.tr, model = GLMBoostModel)