# boost_control

0th

Percentile

##### Control Hyper-parameters for Boosting Algorithms

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

Keywords
misc
##### 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. If mstop = 0, the offset model is returned.

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 to risks based on the out-of-bag (all observations with zero weights) and none to no risk computations at all.

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 number giving the risk difference that needs to be exceeded.

center

deprecated. A logical indicating if the numerical covariates should be mean centered before fitting. Only implemented for glmboost. In blackboost centering is not needed. In gamboost centering is only needed if bols base-learners are specified without intercept. In this case centering of the covariates is essential and should be done manually (at the moment). Will be removed in favour of a corresponding argument in glmboost in the future (and gives a warning).

trace

a logical triggering printout of status information during the fitting process.

##### Details

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

##### Value

An object of class boost_control, a list.

mboost