relatively new to this and this question has been plaguing me.
Say I have a dataset with feature A, feature B, and feature C. I need to scale for my model. Based on their distributions, feature A is suited to robust scaling, feature C is suited to standardization and feature B is suited to log transformation. I have been told that it is acceptable to use different scalers or transformations on different features; that it is okay to scale feature A using a robust scaler and then to transform feature B using log transform and to standardize feature C.
If this is indeed okay (and I am not sure), why? It seems a bit counter-intuitive to me- won't this change how the variables relate to one another? I would have assumed (before I was told otherwise) that one scaler had to be applied to each feature to keep the relationships intact.
I would really love a discussion or explanation if at all possible- seeing the math would probably help me too. I know this is very theoretical but it truly is driving me nuts.