I have been able to successfully test my model in KSVM for accuracy, but I'm not sure how to understand what is actually meant by this output. I tested this using my data also using C=100 and the accuracy does not change whatsoever, but I do understand that I should be looking for more understanding on how the C affects the hyperplane. I have read a couple of great articles about SVM and how to interpret, but just not sure where to look in my output for the actual details of the hyperplane.
> data<-as.data.frame(data)
> model<-ksvm(as.matrix(data[,1:10]),as.factor(data[,11]),type="C-svc",kernel="vanilladot",C=.10,scaled=TRUE)
Setting default kernel parameters
> a<-colSums(model@xmatrix[[1]])*model@coef[[1]]
> a0<- -model@b
> pred<-predict(model,data[,1:10])
> sum(pred == data[,11])/nrow(data)
[1] 0.8639144
I am so thankful for any thoughts on this you all may have to offer.