Machine Learning with R Cookbook - Second Edition

How it works...

In this recipe, we implement a simple script performing 10-fold cross-validations. We first generate an index with 10 fold with the cut function. Then, we implement a for loop to perform a 10-fold cross-validation 10 times. Within the loop, we first apply svm on 9 folds of data as the training set. We then use the fitted model to predict the label of the rest of the data (the testing dataset). Next, we use the sum of the correctly predicted labels to generate the accuracy. As a result of this, the loop stores 10 generated accuracies. Finally, we use the mean function to retrieve the average of the accuracies.