bst v0.3-14

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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.

Functions in bst

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

Type Package
Date 2016-09-12
VignetteBuilder R.rsp, knitr
License GPL (>= 2)
LazyLoad yes
Packaged 2016-09-13 13:56:12 UTC; zhu
NeedsCompilation no
Repository CRAN
Date/Publication 2016-09-21 16:05:05

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