Pre-processing of features with cross-validation I want to determine the best values of the hyperparameters of my model. In that respect, I am using a 5-fold CV approach + a test set defined as unseen data. That is, you have 5 folds:

*

*Fold 1: Learning set (1) + Validation set (1)

*Fold 2: Learning set (2) + Validation set (2)

*Fold 3: Learning set (3) + Validation set (3)

*Fold 4: Learning set (4) + Validation set (4)

*Fold 5: Learning set (5) + Validation set (5)
and the test set.

I want to normalize my features before determining the best values of the hyperparameters. In each fold, how should I normalize my feature?
1- I normalize the learning set + validation set by using only the learning set
or
2- I normalize the learning set + validation set by using only the learning set + validation set
Now, I have the best values of my hyperparameters and I want to test the accuracy of my model on the test set. Should I combine my learning set + validation set? If so, how should I normalize the features in the test set?
1- I normalize the test set by using only the learning set
or
2- I normalize the test set by using the merge of the learning set + validation set

Everything you do to the training set needs to be done without the test set, and validation set data. So the proper way to do this is to estimate the mean and SD for normalization of your data on the training set and then use these estimates to also normalize the test set and the validation set. It might not always make a huge difference, nevertheless, this is the correct procedure.
edit: if you are relearning the model on the train+test set, than you can use train+test set to estimate your normalization parameters and apply them to the validation set
