fastAdaboost is a blazingly fast implementation of adaboost for R. It uses C++ code in the backend to provide an implementation of adaboost that is about 100 times faster than native R based libraries currently available. This is especially useful if your data size is large. fastAdaboost works only for binary classification tasks presently. It implements Freund and Schapire's Adaboost.M1 and Zhu et. al's SAMME.R (real adaboost) algorithms.
It is not submitted to CRAN yet.
devtools::install_github("souravc83/fastBoost")library("fastBoost")
num_each <- 1000
fakedata <- data.frame( X=c(rnorm(num_each,0,1),rnorm(num_each,1.5,1)), Y=c(rep(0,num_each),rep(1,num_each) ) )
fakedata$Y <- factor(fakedata$Y)
#run adaboost
A <- adaboost(Y~X, fakedata, 10)
#print(A)
pred <- predict(A,newdata=fakedata)
print(paste("Adaboost Error on fakedata:",pred$error))
B <- real_adaboost(Y~X, fakedata, 10)
pred_real <- predict(B,fakedata)
print(paste("Real Adaboost Error on fakedata:", pred_real$error))