When I am cleaning my data, I have some features which contain large numbers and some features that are binary. Should I scale the large features and then add the binary columns or just scale them all together?
My fear is that scaling them all together makes the binary features seem less important than they really are.
Note: I am prepping a neural network for binary classification. I am using a sigmoid output and scaling my features from [0,1]