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mboost (version 2.4-1)

boost_control: Control Hyper-parameters for Boosting Algorithms

Description

Definition of the initial number of boosting iterations, step size and other hyper-parameters for boosting algorithms.

Usage

boost_control(mstop = 100, nu = 0.1,
              risk = c("inbag", "oobag", "none"), stopintern = FALSE,
              center = TRUE, trace = FALSE)

Arguments

mstop
an integer giving the number of initial boosting iterations.
nu
a double (between 0 and 1) defining the step size or shrinkage parameter. The default is probably too large for many applications with family = Poisson() and a smaller value is better.
risk
a character indicating how the empirical risk should be computed for each boosting iteration. inbag leads to risks computed for the learning sample (i.e., all non-zero weights), oobag
stopintern
a logical that defines if the boosting algorithm stops internally when the out-of-bag risk in one iteration is larger than the out-of-bag risk in the iteration before. Can also be a positive
center
deprecated. A logical indicating if the numerical covariates should be mean centered before fitting. Only implemented for glmboost. In blackboos
trace
a logical triggering printout of status information during the fitting process.

Value

  • An object of class boost_control, a list.

Details

Objects returned by this function specify hyper-parameters of the boosting algorithms implemented in glmboost, gamboost and blackboost (via the control argument).

See Also

mboost