We will use the e1071 package to build our SVM models. We will start with a linear support vector classifier and then move on to the nonlinear versions. The e1071 package has a nice function for SVM called tune.svm(), which assists in the selection of the tuning parameters/kernel functions. The tune.svm() function from the package uses cross-validation to optimize the tuning parameters. Let's create an object called linear.tune and call it using the summary() function, as follows:
> linear.tune <- tune.svm(type ~ ., data = train, kernel = "linear", cost = c(0.001, 0.01, 0.1, 1, 5, 10)) > summary(linear.tune) Parameter tuning of 'svm': - sampling method: 10-fold cross validation - best parameters: cost 1 - best performance: ...
