Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to cross-validate the telecom churn dataset:

1. Split the index into 10 fold using the cut function:

        > ind = cut(1:nrow(churnTrain), breaks=10, labels=F)

2. Next, use the for loop to perform a 10 fold cross-validation, repeated 10 times:

        > accuracies = c()
        > for (i in 1:10) {+   fit = svm(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)
        + }  

3. You can then print the accuracies:

        > accuracies
        Output
         [1] 0.9341317 0.8948949 0.8978979 0.9459459 0.9219219 0.9281437 0.9219219 0.9249249 ...