There are many articles explaining the difference between regression and ordinal classification, most of them mentioned that regression is for continuous response while ordinal classification is for discrete response. However, I think it in another way, and that's my question:
As more and more discrete values added to the response set, it is more and more approximate a continuous response. Shall we really separate regression and ordinal classification as two different worlds? What models lie between regression and ordinal classification? What if i have a large number of discrete values in response variable (but still no rigorous continuous), integer 1 to 10000 for instance, what kinds of model can handles this issue?