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library(e1071)

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dataPath<-"data/"
dat <- read.csv(paste0(dataPath,"test_sample.csv"))

dat<-as.data.frame(dat)
dat$class<-as.factor(dat$class)
dat

plot(dat$x,dat$y,col="orange",pch=16,ylim=c(0,1),xlim=c(0,1))
points(dat$x[dat$class==1],dat$y[dat$class==1],col="blue",pch=16)

m1<-svm(class~.  , data=dat)
m1.pred<-predict(m1)

plot(m1,dat)

classAgreement(table(pred = m1.pred, true = dat[,"class"]))$diag 

[1] 0.894

set.seed(1)
svmTuned <- tune.svm(class~., data = dat, gamma = 10^(-3:-1), cost = 5*(2:5))
summary(svmTuned)

Parameter tuning of ‘svm’:

- sampling method: 10-fold cross validation 

- best parameters:

- best performance: 0.115 

- Detailed performance results:

NA

plot(svmTuned$best.model,dat)

set.seed(1)
predict <- predict(svmTuned$best.model, dat[,!names(dat)=="class"])
classAgreement(table(pred = predict, true = dat[,"class"]))$diag

[1] 0.889