rbstpath | R Documentation |
Gradient boosting path for optimizing robust loss functions with componentwise linear, smoothing splines, tree models as base learners. See details below before use.
rbstpath(x, y, rmstop=seq(40, 400, by=20), ctrl=bst_control(), del=1e-16, ...)
x |
a data frame containing the variables in the model. |
y |
vector of responses. |
rmstop |
vector of boosting iterations |
ctrl |
an object of class |
del |
convergency criteria |
... |
arguments passed to |
This function invokes rbst
with mstop
being each element of vector rmstop
. It can provide different paths. Thus rmstop
serves as another hyper-parameter. However, the most important hyper-parameter is the loss truncation point or the point determines the level of nonconvexity. This is an experimental function and may not be needed in practice.
A length rmstop
vector of lists with each element being an object of class rbst
.
Zhu Wang
rbst
x <- matrix(rnorm(100*5),ncol=5) c <- 2*x[,1] p <- exp(c)/(exp(c)+exp(-c)) y <- rbinom(100,1,p) y[y != 1] <- -1 y[1:10] <- -y[1:10] x <- as.data.frame(x) dat.m <- bst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls") predict(dat.m) dat.m1 <- bst(x, y, ctrl = bst_control(twinboost=TRUE, coefir=coef(dat.m), xselect.init = dat.m$xselect, mstop=50)) dat.m2 <- rbst(x, y, ctrl = bst_control(mstop=50, s=0, trace=TRUE), rfamily = "thinge", learner = "ls") predict(dat.m2) rmstop <- seq(10, 40, by=10) dat.m3 <- rbstpath(x, y, rmstop, ctrl=bst_control(s=0), rfamily = "thinge", learner = "ls")
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