Description Usage Arguments Value Author(s) References Examples
Fit a predictive model with robust boosting algorithm. For loss functions in the CC-family (concave-convex), apply composite optimization by conjugation operator (COCO), where optimization is conducted by functional descent boosting algorithm. Models include the generalized linear models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
x |
input matrix, of dimension nobs x nvars; each row is an observation vector. Can accept |
y |
response variable. Quantitative for |
weights |
vector of nobs with non-negative weights |
cfun |
concave component of CC-family, can be |
s |
tuning parameter of |
delta |
a small positive number provided by user only if |
dfun |
type of convex component in the CC-family, can be |
iter |
number of iteration in the COCO algorithm |
nrounds |
boosting iterations |
del |
convergency criteria in the COCO algorithm |
trace |
if |
... |
other arguments passing to |
An object with S3 class xgboost
.
weight_update |
weight in the last iteration of the COCO algorithm |
Zhu Wang
Maintainer: Zhu Wang zhuwang@gmail.com
Wang, Zhu (2021), Unified Robust Boosting, arXiv eprint, https://arxiv.org/abs/2101.07718
1 2 3 4 5 6 7 8 | x <- matrix(rnorm(100*2),100,2)
g2 <- sample(c(0,1),100,replace=TRUE)
fit1 <- ccboost(x, g2, cfun="acave",s=0.5, dfun="gaussian", trace=TRUE,
verbose=0, max.depth=1, nrounds=50)
fit2 <- ccboost(x, g2, cfun="acave",s=0.5, dfun="binomial", trace=TRUE,
verbose=0, max.depth=1, nrounds=50)
fit3 <- ccboost(x, g2, cfun="acave",s=0.5, dfun="poisson", trace=TRUE,
verbose=0, max.depth=1, nrounds=50)
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