16 Support Vector Machines in R Performance of 'svm' 100 0.40 80 0.35 0.30 60 0.25 40 0.20 20 0.15 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 logio(7) Figure 3:Contour plot of the error landscape resulting from a grid search on a hyper- parameter range. (162151) Number of Classes: 2 Levels: nonspam spam 10-fold cross-validation on training data: Total Accuracy:91.6 Single Accuracies: 94919290919192909293 pred <-predict(model,spam_test) (acc <-table(pred,spam_test$type)) pred nonspam spam nonspam 2075196 spam 1151215 classAgreement(acc)16 Support Vector Machines in R 0.15 0.20 0.25 0.30 0.35 0.40 −6.0 −5.5 −5.0 −4.5 −4.0 −3.5 −3.0 20 40 60 80 100 Performance of ‘svm' log10(γ) C Figure 3: Contour plot of the error landscape resulting from a grid search on a hyper￾parameter range. ( 162 151 ) Number of Classes: 2 Levels: nonspam spam 10-fold cross-validation on training data: Total Accuracy: 91.6 Single Accuracies: 94 91 92 90 91 91 92 90 92 93 > pred <- predict(model, spam_test) > (acc <- table(pred, spam_test$type)) pred nonspam spam nonspam 2075 196 spam 115 1215 > classAgreement(acc)