Is there ever a reason to solve a regression problem as a classification problem? Would there ever be a reason to repose a regression forecasting problem as a classification problem by, for example, using classes to describe if sales in one year are 10%,50%,90%, or >100% of current levels. Those things could naturally be inferred from the results of a regression, yet I have seen people doing such classifications that actually feels more like regression problems.
I'm a newbie to the forum and ML in general so I hope I did everything right when posing the question :)

You can discretize the regression problem for example into the classification of having an illness "yes" and "no", by this making it possible to read the probabilities of each class (yes/no) from an ML classification model.
You might have perhaps ten different intensities of this illness and you know the thresholds for them from experience so that you have labels, perhaps using a point system over many input columns or just experience over years.
The advantage of a classification model is that each class out of the ten classes gets its own probability, while in a regression model, you do not see the probability, but you get just the one most probable predicted value instead.
