Nested cross-validation - how is it different from model selection via kfold CV on the training set? 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?
 A: 2 repetitions in outer loop mean that you repeat  your 5-fold CV 2 times on the whole train set. Each time subdivision into folds will be different.
This is mainly used for better estimations of model performance, like running statistical tests on whether one model performs statistically-significantly better than another. 
Nested CV is not critically important if your data set is large and without outliers. If your data do have outliers, than cross validation performance may be drastically different depending on what fold/folds these outliers are in. Therefore you repeat CV several times.
A: 5x2cv as far as I have seen in the literature, always refer to a 5 repetition of a 2-fold. There is no nesting at all. do a 2-fold (50/50 split between train and test), repeat it 4 more times. The 5x2cv was popularised by the paper Approximate statistical tests for comparing supervised classification learning algorithms by Dietterich  as a way of obtaining not only a good estimate of the generalisation error but also a good estimate of the variance of that error (in order to perform statistical tests)
