bst v0.3-16

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Gradient Boosting

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Functions in bst

Name Description
cv.mhingebst Cross-Validation for Multi-class Hinge Boosting
mbst Boosting for Multi-Classification
ex1data Generating Three-class Data with 50 Predictors
cv.mada Cross-Validation for one-vs-all AdaBoost with multi-class problem
cv.mbst Cross-Validation for Multi-class Boosting
mhingebst Boosting for Multi-class Classification
bst Boosting for Classification and Regression
mhingeova Multi-class HingeBoost
bst.sel Function to select number of predictors
bfunc Compute upper bound of second derivative of loss
loss Internal Function
cv.rbst Cross-Validation for Nonconvex Loss Boosting
bst_control Control Parameters for Boosting
cv.rmbst Cross-Validation for Nonconvex Multi-class Loss Boosting
rbstpath Robust Boosting Path for Nonconvex Loss Functions
cv.bst Cross-Validation for Boosting
nsel Find Number of Variables In Multi-class Boosting Iterations
rbst Robust Boosting for Robust Loss Functions
rmbst Robust Boosting for Multi-class Robust Loss Functions
evalerr Compute prediction errors
cv.mhingeova Cross-Validation for one-vs-all HingeBoost with multi-class problem
mada Multi-class AdaBoost
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Vignettes of bst

Name
bst.bib
cancer.Rnw
khan.Rnw
mcl.Rnw
pros.Rnw
static_cancer.pdf.asis
static_khan.pdf.asis
static_mcl.pdf.asis
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Details

Type Package
Date 2019-01-14
VignetteBuilder R.rsp, knitr
License GPL (>= 2)
LazyLoad yes
Packaged 2019-01-15 19:50:40 UTC; zhu
NeedsCompilation no
Repository CRAN
Date/Publication 2019-01-16 09:10:10 UTC

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