adaboost: Adaboost.M1 algorithm

In fastAdaboost: a Fast Implementation of 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 be a factor with exactly two levels. The final classifier is a linear combination of weak decision tree classifiers.

Value

object of class adaboost

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)

Example output

fastAdaboost documentation built on May 2, 2019, 3:33 p.m.