Apologies if I haven't got the terminology quite right. I have a question about Neural Networks, and I'm not sure exactly the best way to ask it!
Hypothetically, let's say I have a dataset of houses on the property market. One of the features could be number of bathrooms, and another is floor plan area.
If I were using linear regression, I might also include a compound feature e.g. number of bathrooms / floor plan area. I could come up with all sorts of combinations of feature products, and create higher order polynomials.
My question is - is this necessary for neural networks? Or standard practise? Or useful?
Neural networks are such a black box I'm not sure if it would just "work this out" or not.
If there isn't a simple answer, I would be grateful at least for anything you can tell me about how this phenomenon is referred to. Are they compound features? Or computed features? I really don't know...