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bst (version 0.3-13)

Gradient Boosting

Description

Functional gradient descent algorithm for a variety of convex and nonconvex loss functions, for both classical and robust regression and classification problems. HingeBoost is implemented for binary and multi-class classification, with unequal misclassification costs for binary case. The algorithm can fit linear and nonlinear classifiers.

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Version

Install

install.packages('bst')

Monthly Downloads

3,759

Version

0.3-13

License

GPL (>= 2)

Maintainer

Zhu Wang

Last Published

February 28th, 2016

Functions in bst (0.3-13)

Function to select number of predictors

Multi-class HingeBoost

Multi-class AdaBoost

Cross-Validation for Multi-class Hinge Boosting

Cross-Validation for one-vs-all AdaBoost with multi-class problem

Boosting for Classification and Regression

Cross-Validation for Truncated Loss Boosting

Cross-Validation for Boosting

Boosting for Classification and Regression

Internal Function

Robust Boosting Path for Truncated Loss Functions

Robust Boosting for Multi-class Truncated Loss Functions

Generating Three-class Data with 50 Predictors

Control Parameters for Boosting

Cross-Validation for Truncated Multi-class Loss Boosting

Boosting for Multi-Classification

Robust Boosting for Truncated Loss Functions

Find Number of Variables In Multi-class Boosting Iterations

Boosting for Multi-class Classification

Cross-Validation for Multi-class Boosting

Cross-Validation for one-vs-all HingeBoost with multi-class problem