# Am I supposed to scale binary features alongside other numerical features?

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]

• Typically binary features are represented as $\{0,1\}$. If you scale such features into 0 to 1 range then they would not be affected. Could you clarify whether you are doing anything different (such as starting with $\{-1,1\}$, or scaling to mean 0, standard deviation 1)? – Neil Slater Sep 15 '17 at 6:43