I often see people talking about 5x2 cross-validation as a special case of nested cross validation.
I assume the first number (here: 5) refers to the number of folds in the inner loop and the second number (here: 2) refers to the number of folds in the outer loop? So, how is this different from a "traditional" model selection and evaluation approach? By "traditional", I mean
- split the dataset into a separate training (e.g., 80%) and test set
- use k-fold cross-validation (e.g., k=10) for hyperparameter tuning and model selection on the training set
- evaluate generalization performance of the selected model using the test set
Isn't 5x2 exactly the same except that the test and training set have equal size if k=2?