I am performing nested cross-validation, and I know that the idea behind it is to see how the model generalizes. For that, we don't only shuffle the training data but we also do shuffle the testing data.
Having this said, should nested cv be done on the whole data or just on the training part (like split the the data into 80% training and 20% testing and apply nested cv only on the 80% training). However I feel this is illogical since the whole idea is to see how well it generalizes.