I am getting a little confused with the idea of composite variables. If you have a bunch of (hyptertheical) features for house price regression such as #rooms, location, proximity to school, house area and yard area. I know you could combine some of these into composite variables, such as total house area (Yard + house) or maybe some empirical classification which takes house area, number of rooms and proximity to school, applies ratings to the input values and returns a number.
I know, in some situations such variables may improve regressor performance (as mentioned by Andrew Ng in one of his excellent videos on ML Regression).
However, I assume you would then not include the original features if you use the compound features? Does it matter how the composite feature was calculated or relates the independent features? I have been trying to find some references for this problem, but I have yet to find any. Can anyone else point me in the right direction?