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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:

- input
- log(input)
- sqrt(input)

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?

Beautiful, thanks for adding this word to my vocabulary! – William Entriken – 2017-07-26T00:54:10.657