fastAdaboost (version 1.0.0)

real_adaboost: Real Adaboost algorithm

Description

Implements Zhu et al's real adaboost or SAMME.R algorithm

Usage

real_adaboost(formula, data, nIter, ...)

Arguments

formula

Formula for models

data

Input dataframe

nIter

no. of classifiers

...

other optional arguments, not implemented now

Value

object of class real_adaboost

Details

This implements the real adaboost algorithm for a binary classification task. The target variable must be a factor with exactly two levels. The final classifier is a linear combination of weak decision tree classifiers. Real adaboost uses the class probabilities of the weak classifiers to iteratively update example weights. It has been found to have lower generalization errors than adaboost.m1 for the same number of iterations.

References

Zhu, Ji, et al. “Multi-class adaboost” Ann Arbor 1001.48109 (2006): 1612.

See Also

adaboost,predict.real_adaboost

Examples

fakedata <- data.frame( X=c(rnorm(100,0,1),rnorm(100,1,1)), Y=c(rep(0,100),rep(1,100) ) )
fakedata$Y <- factor(fakedata$Y)
test_adaboost <- real_adaboost(Y~X, data=fakedata,10)