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 :)

One counter-example that I see often:
Outcomes that are proportions (eg 10% = 2/20, 20%= 1/5, etc) should not get dumped through OLS, instead use a logistic regression with the denominator specified. This will weight the cases correctly even though they have different variances.
OTOH, logistic regression is a proper regression model, despite it mostly being taught as a classifier. So maybe this doesn't count.
