Displaying 1 result from an estimated 1 matches for "save_ensembless".
2010 Feb 03
0
mboost: how to implement cost-sensitive boosting family
..., w = 1)
{
ifelse(y==1, 0.001*exp(0.001*y*f)/((1+exp(0.001*y*f))^2), exp(-y*f)/(1+exp(-y*f)) )
}
CSAdaExp <- Family(ngradient = ngradient, loss = loss);
model.blackboost <- blackboost(tr[,1:DIM], tr.y, family=CSAdaExp,
weights=NULL, control=boost_control(mstop=MSTOP,
nu=0.1,savedata=TRUE,save_ensembless=TRUE,trace=TRUE),
tree_controls=ctree_control(teststat = "max",testtype =
"Teststatistic",mincriterion = 0,minsplit = 2000, minbucket =
700,maxdepth = TREEDEPTH));
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regards,
Yuchun Tang, Ph.D.
Principal Engineer, Lead
McAfee, Inc.
4501 North P...