Usage
boost_control(mstop = 100, nu = 0.1, constraint = FALSE,
risk = c("inbag", "oobag", "none"),
savedata = TRUE, center = FALSE, trace = FALSE,
save_ensembless=TRUE)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 $(-1, +1)$.
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
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. In blackboost trace
a logical triggering printout of status information during
the fitting process.
save_ensembless
a logical indicating if the list of
baselearners should be saved and returned. This list is generally
needed but can be suppressed to reduce memory usage (not recommended).