Robust Boosting Path for Truncated Loss Functions

Description

Gradient boosting path for optimizing robust loss functions with componentwise linear, smoothing splines, tree models as base learners.

Usage

rbstpath(x, y, rmstop=seq(40, 400, by=20), ctrl=bst_control(), del=1e-16, ...)

Arguments

Details

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.

Value

A length rmstop vector of lists with each element being an object of class rbst.

Author(s)

Zhu Wang

See Also

rbst

Examples

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, rfamily = "thinge", learner = "ls")

Results