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?
In part, because it can be prohibitively expensive to generate those results. And then also laziness. I used to go for a minimum of 3 random starts, until I was told to stop wasting resources in our cluster.