I am trying to predict an output value based on several continuously-valued inputs using a regression model.
I am not sure what approach is appropriate to scale/transform the input data for the regression. Let's just pretend that it is unlabeled data.
My most naive approach would be to add each input multiple times:
and then let the regression model worry about finding which flavor of each input (if any) is significant.
What are the risks with using this approach?