Machine Learning with R Cookbook - Second Edition

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If you wish to perform the k-fold validation with the use of other models, simply replace the line to generate the fit variable to whatever classifier you prefer. For example, if you would like to assess the Naïve Bayes model with a 10-fold cross-validation, you just need to replace the calling function from svm to naiveBayes:

> for (i in 1:10) {
+   fit = naiveBayes(churn ~., churnTrain[ind != i,])
+   predictions = predict(fit, churnTrain[ind == i, ! names(churnTrain) %in% c("churn")])
+   correct_count = sum(predictions == churnTrain[ind == i,c("churn")])
+   accuracies = append(correct_count / nrow(churnTrain[ind == i,]), accuracies)
+ }