adaBoost: Adaboost.M1 algorithm

In souravc83/fastBoost: a fast implementation of adaboost.M1 and real adaboost

Description

Implements Freund and Schapire's Adaboost.M1 algorithm

Usage

adaboost(formula, data, nIter, ...)

Arguments

formula	Formula for models
data	Input dataframe
nIter	no. of classifiers
...	other optional arguments, not implemented now

Details

This implements the Adaboost.M1 algorithm for a binary classification task. The target variable must a a factor with exactly two levels. The final classifier is a linear combination of weak decision tree classifiers.

References

Freund, Y. and Schapire, R.E. (1996):“Experiments with a new boosting algorithm” . In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann.

See Also

real_adaboost,predict.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 <- adaboost(Y~X, data=fakedata,10)

souravc83/fastBoost documentation built on May 30, 2019, 6:34 a.m.