boost_control(mstop = 100, nu = 0.1,
risk = c("inbag", "oobag", "none"), stopintern = FALSE,
center = TRUE, trace = FALSE)
family = Poisson()
and a smaller value is better.inbag
leads to
risks computed for the learning sample (i.e., all non-zero weights),
oobag
boost_control
, a list.glmboost
,
gamboost
and blackboost
(via the control
argument).mboost