Often when I read ML papers the authors compare their results against a benchmark (e.g. using RMSE, accuracy, …) and say “our results improved with our new method by X%”. Nobody makes a significance test if the new method Y outperforms benchmark Z. Is there a reason why? Especially when you break your results down e.g. to the anaylsis of certain classes in object classification this seems important for me. Or do I overlook something?
They don’t even report if ANY kind of statistically sane validation method is used when selecting model parameters (usually a single number is reported) and you expect rigorous statistical significance testing? That.is.bold.