I have few questions concerning the selection of hyperparameters for predictions in cross validation.
If I understand well, during the CV, you just create folds (inner and outer for a nested CV), and then, for each fold, you train your algorithm, and make predictions on the test set, right ? Then, to estimate unbiased performances of your model, you calculate the average and the SD of all folds. And, with this "average" performance, what do you do with this ? I mean, I can't extract global "hyperparameters" optimized I guess... And can't i predict on a whole new dataset then ?
If someone could explain me the mechanism behind this^^
For example, I computed manually a nested cross validation (5 folds inner and 5 outer) on my datas, and I obtain these results on my validation set to select the best model. These are average with sd performances. What can I do with this ?