I know this is a basic problem, but still could not find answer, I feel like most books/tutorials avoid talking about scaling the output feature instead they just mention scaling input features.
So my question is: in Machine/Deep Learning regression problems should the output (target feature) be scaled(for example by using MinMax or Standard scaler) or not?
And if scaling should be applied only to some ML/DL architectures, than to which ones?