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bst (version 0.3-14)

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.

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Version

Install

install.packages('bst')

Monthly Downloads

1,249

Version

0.3-14

License

GPL (>= 2)

Maintainer

Zhu Wang

Last Published

September 21st, 2016

Functions in bst (0.3-14)

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

mbst

Boosting for Multi-Classification