Does normalizing data (to have zero mean and unity standard deviation) prior to performing a repeated k-fold cross-validation have any negative conquences such as overfitting?
Note: this is for a situation where #cases > total #features
I am transforming some of my data using a log transform, then normalizing all data as above. I am then performing feature selection. Next I apply the selected features and normalized data to a repeated 10-fold cross-validation to try and estimate generalized classifier performance and am concerned that using all data to normalize may not be appropriate. Should I normalize the test data for each fold using normalizing data obtained from the training data for that fold?
Any opinions gratefully received! Apologies if this question seems obvious.
Edit: On testing this (in line with suggestions below) I found that normalization prior to CV did not make much difference performance-wise when compared to normalization within CV.