SVM

library(e1071)
data(iris)
set.seed(1234)
rndSample<-sample(1:nrow(iris),100)
tr<-iris[rndSample,]
ts<-iris[-rndSample,]
s<-svm(Species~.,tr, cost=20,kernel="polynomial",degree=3)
ps<-predict(s,ts)
cm<-table(ps,ts$Species)
precision<-100*(1-sum(diag(cm))/sum(cm))
precision

## [1] 2

library(e1071)
data(Boston, package = 'MASS')
set.seed(1234)
sp<-sample(1:nrow(Boston),354)
tr<-Boston[sp,]
ts<-Boston[-sp,]
s<-svm(medv~.,tr)
ps<-predict(s,ts)