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bst (version 0.3-16)

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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Version

Install

install.packages('bst')

Monthly Downloads

1,184

Version

0.3-16

License

GPL (>= 2)

Maintainer

Zhu Wang

Last Published

January 16th, 2019

Functions in bst (0.3-16)

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