I'm asking because classification problems have very concrete metrics like accuracy that are totally transparent to understand.
Whereas regression models seem to have a very large number of possible evaluation strategies and to me at least it is not clear which (if any) of them is as reliable/interpretable as accuracy is in classification problems.
- Regular loss (e.g MAE): MAE is potentially quite interpretable, but again interpretation depends upon distribution statistics which vary across regression problems.
- MAPE/Relative Loss: This is interesting and is potentially decently similar to accuracy. Yet it has obvious draw backs, like the true value being extremely small causing explosion of loss values & there being no incorporation of overall distribution statistics for the output values.
- Chi-squared test: I like the idea of this but I have not seen it used at all for NN regression for some reason. I'm not sure why and I'm curious if people think it would be a good idea to use it for that.
- (adjusted) R^2 coefficient: Another statistic that seems great in theory, but again I see almost never being used for NNs and I'm not sure why. This has the great advantage of being a 'bounded'/'normalized' metric like accuracy and in theory is should be just as interpretable. Why is it not used for NNs?