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bst (version 0.3-24)
Gradient Boosting
Description
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)
, Wang (2012)
, Wang (2018)
, Wang (2018)
.
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Install
install.packages('bst')
Monthly Downloads
1,566
Version
0.3-24
License
GPL (>= 2)
Maintainer
Zhu Wang
Last Published
January 6th, 2023
Functions in bst (0.3-24)
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bst_control
Control Parameters for Boosting
cv.bst
Cross-Validation for Boosting
mhingeova
Multi-class HingeBoost
bst
Boosting for Classification and Regression
evalerr
Compute prediction errors
ex1data
Generating Three-class Data with 50 Predictors
mada
Multi-class AdaBoost
rbst
Robust Boosting for Robust Loss Functions
bst.sel
Function to select number of predictors
mhingebst
Boosting for Multi-class Classification
cv.mhingebst
Cross-Validation for Multi-class Hinge Boosting
bfunc
Compute upper bound of second derivative of loss
cv.mada
Cross-Validation for one-vs-all AdaBoost with multi-class problem
cv.rbst
Cross-Validation for Nonconvex Loss Boosting
cv.mhingeova
Cross-Validation for one-vs-all HingeBoost with multi-class problem
cv.mbst
Cross-Validation for Multi-class Boosting
cv.rmbst
Cross-Validation for Nonconvex Multi-class Loss Boosting
mbst
Boosting for Multi-Classification
rbstpath
Robust Boosting Path for Nonconvex Loss Functions
loss
Internal Function
nsel
Find Number of Variables In Multi-class Boosting Iterations
rmbst
Robust Boosting for Multi-class Robust Loss Functions