Currently I'm doing some simple feature selection based on correlation between features and variance within one feature. I'm applying this on the whole dataset used for model building before creating the cross-validation.My question now is if this is acceptable workflow or can significantly affect the CV stats suggesting a better model than it actually is? Is it technically better to do the CV-split and only then select features on the training set for that split to not leak information?
Feature selection: Information leaking if done before CV-split?
feature selection;cross validation