boost_control {mboost}R Documentation

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, constraint = FALSE, 
              risk = c("inbag", "oobag", "none"), 
              savedata = TRUE, center = FALSE, 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.
constraint a logical indicating whether the working responses should be restricted to newline (-1, +1).
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.
savedata a logical, should the data be saved in the returned object?
center a logical indicating if the numerical covariates should be mean centered before fitting. Only implemented for glmboost and gamboost.
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.


[Package mboost version 1.0-1 Index]