adaBoost: Adaboost algorithm

In freestats: Statistical algorithms used in common data mining course

Description

Do classification using adaboost algorithm with decisionStump as weak learner

Usage

adaBoost(train = decisionStump, dat.train, y.train, B = 10, ...)

Arguments

train	Function of weak learner that would be used in adaboost, must have form train(dat.train,w,y.train)
dat.train	Training data set
y.train	Label for training data set
B	Number of weak learners that will used
...	Other parameters that need to passed in train function

Details

Train function can be any weak learner algorithm. For now, train function must has form train(X,w,y,...). see more in decisionStump

If you have any good weak learner but can't use it in this function, feel free to let me know.

Value

alpha	The weight for different weak learners
allPars	A list of parameters for different weak learners

Author(s)

Xiaoyao Yang

Examples

set.seed(1024)
z <- runif(n=5)
mydata <- fakedata(w=z,n=100)
X<- mydata$S[,1:4]
y <- mydata$y
res <- adaBoost(dat.train=X,y.train=y,B=3)

Example output

freestats documentation built on May 2, 2019, 1:18 p.m.