I'm aware that nested cross-validation is used for hyperparameter tuning and model selection and that repeated k-fold cross-validation is used to improve the estimated performance of the model.
My question is: can nested cross-validation be repeated?
As described here, nested K-fold cross validation works as follows:
1. Partition the training set into ‘K’ subsets 2. In each iteration, take ‘K minus 1’ subsets for model training, and keep 1 subset (holdout set) for model testing. 3. Further partition the ‘K minus 1’ training set into ‘K’ subsets, and iteratively use the new ‘K minus 1’ subset and the ‘validation set’ for parameter tuning (grid search). The best parameter identified in this step is used to test on the holdout set in step 2.
Can this process be repeated N times, for different partitions of the dataset, just as you would do in repetead cross-validation?
I came across this question which seems related to my question, but unfortunately has no accepted answer.
I also came across this paper where they seem to describe the procedure I'm asking about, though I haven't seen it described elsewhere.