# NOT RUN {
data(iris)
x=iris[,1:4]
y=factor(iris[,5])
train=sample(1:dim(iris)[1],100)
xTrain=x[train,]
xTest=x[-train,]
yTrain=y[train]
yTest=y[-train]
# Center and scale data
s=scale(xTrain,center=TRUE,scale=TRUE)
# Sparse Logistic Regression
t=6
co=heuristicC(s)
m=LiblineaR(data=s,labels=yTrain,type=t,cost=co,bias=TRUE,verbose=FALSE)
# Scale the test data
s2=scale(xTest,attr(s,"scaled:center"),attr(s,"scaled:scale"))
# Make prediction
p=predict(m,s2)
# Display confusion matrix
res=table(p$predictions,yTest)
print(res)
# Compute Balanced Classification Rate
BCR=mean(c(res[1,1]/sum(res[,1]),res[2,2]/sum(res[,2]),res[3,3]/sum(res[,3])))
print(BCR)
# }
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