An MM algorithm operates by creating a convex surrogate function that majorizes the nonconvex objective function. When the surrogate function is minimized with gradient boosting algorithm, the desired objective function is decreased. The MM algorithm contains difference of convex (DC) algorithm for rfamily=c("tgaussian", "thuber","thinge", "tbinom", "binomd", "texpo", "tpoisson")
and quadratic majorization boosting algorithm (QMBA) for rfamily=c("clossR", "closs", "gloss", "qloss")
.
rfamily
= "tgaussian" for truncated square error loss, "thuber" for truncated Huber loss, "thinge" for truncated hinge loss, "tbinom" for truncated logistic loss, "binomd" for logistic difference loss, "texpo" for truncated exponential loss, "tpoisson" for truncated Poisson loss, "clossR" for C-loss in regression, "closs" for C-loss in classification, "gloss" for G-loss, "qloss" for Q-loss.
s
must be a numeric value to be specified in bst_control
. For rfamily="thinge", "tbinom", "texpo"
s < 0
. For rfamily="binomd", "tpoisson", "closs", "qloss", "clossR"
, s > 0
and for rfamily="gloss"
, s > 1
. Some suggested s
values: "thinge"= -1, "tbinom"= -log(3), "binomd"= log(4), "texpo"= log(0.5), "closs"=1, "gloss"=1.5, "qloss"=2, "clossR"=1.