Real Adaboost algorithm
Implements Zhu et al's real adaboost or SAMME.R algorithm
real_adaboost(formula, data, nIter, ...)
formula |
Formula for models |
data |
Input dataframe |
nIter |
no. of classifiers |
... |
other optional arguments, not implemented now |
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.
object of class real_adaboost
Zhu, Ji, et al. “Multi-class adaboost” Ann Arbor 1001.48109 (2006): 1612.
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)
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