Machine Learning with R Cookbook - Second Edition

Estimating model performance with k-fold cross-validation

The k-fold cross-validation technique is a common technique used to estimate the performance of a classifier as it overcomes the problem of over-fitting. For k-fold cross-validation, the method does not use the entire dataset to build the model; instead it splits the data into a training dataset and a testing dataset. Therefore, the model built with a training dataset can then be used to assess the performance of the model on the testing dataset. By performing n repeats of the k-fold validation, we can then use the average of n accuracies to truly assess the performance of the built model. In this recipe, we will illustrate how to perform a k-fold cross-validation.