Should I center/scale dummy variables?
Yes, when the model requires the parameters to be on the same scale
- regularized models (
glmnet and like) have penalties on the sum of the slope values
- nearest neighbor models use distance values and kernel methods (e.g. SVMs) use dot products
- neural networks usually initialize using random numbers and assume the same scale of predictors
- PLS models chase covariance and assume that the variances are the same.
and so on.
There is a decent argument to scale them all to a variance of two but, regardless, for some models you will do them harm if you do not normalize them as needed.
Agreed! Low maintenance is the way to go initially.