rbstpath {bst} | R Documentation |
Robust Boosting Path for Nonconvex Loss Functions
Description
Gradient boosting path for optimizing robust loss functions with componentwise linear, smoothing splines, tree models as base learners. See details below before use.
Usage
rbstpath(x, y, rmstop=seq(40, 400, by=20), ctrl=bst_control(), del=1e-16, ...)
Arguments
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 |
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 or the point determines the level of nonconvexity. This is an experimental function and may not be needed in practice.
Value
A length rmstop
vector of lists with each element being an object of class rbst
.
Author(s)
Zhu Wang
See Also
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, ctrl=bst_control(s=0), rfamily = "thinge", learner = "ls")