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host_fp
2010 Feb 03
0
mboost: how to implement cost-sensitive boosting family
...ackboost(tr[,1:DIM], tr.y, family=CSAdaExp,
weights=tr.w, control=boost_control(mstop=100, nu=0.1),
tree_controls=ctree_control(teststat = "max",testtype =
"Teststatistic",mincriterion = 0,maxdepth = 10));
or
#loss <- function (y, f)
#{
# exp(-y * f * ifelse(y==1,COST_FN,COST_FP))
#}
#ngradient <- function (y, f, w = 1)
#{
# y * ifelse(y==1,COST_FN,COST_FP) * exp(-y * f * ifelse(y==1,COST_FN,COST_FP))
#}
#offset <- function (y, w)
#{
# p <- weighted.mean(y > 0, w)
# 1/(COST_FN+COST_FP) * log(COST_FN*p/COST_FP*(1 - p))
#}
loss <- function (y, f)
{...