Performing cross-validation with the e1071 package

Besides implementing a loop function to perform the k-fold cross-validation, you can use the tuning function (for example, tune.nnet, tune.randomForest, tune.rpart, tune.svm, and tune.knn.) within the e1071 package to obtain the minimum error value. In this recipe, we will illustrate how to use tune.svm to perform the 10-fold cross-validation and obtain the optimum classification model.

Getting ready

In this recipe, we continue to use the telecom churn dataset as the input data source to perform 10-fold cross-validation.

How to do it...

Perform the following steps to retrieve the minimum estimation error using cross-validation:

1. Apply tune.svm on the training dataset, trainset, with the 10-fold cross-validation as the tuning control (if you find an error message, such as could not find function predict.func, please clear the workspace, restart the R session, and reload the e1071 library again):

        > tuned = tune.svm(churn~., data = trainset...