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 :)

Bayesian regression does something like this on a continuous scale.
To each value of the parameter a probability is assigned indicating how likely the parameter has that value.
For instance, for each value of sales (a continuoum of classes) a probability is assigned predicting how likely it is that sales-value/class.
Hypothesis testing is also much like this and a discrete form. One performs regression fits some parameters and subsequently classifies the observation as indictating whether the hypothesis is true or not true. Neyman Pearson hypothesis testing is very explicit with this and compares a null hypothesis and an alternative hypothesis and uses the likelihood ratio to decide between the two hypotheses.
For instance a hypothesis might be that the growth in sales are gonna be more than some hypothetical percentage $x$ and the regression is leading to a rejection or non-rejection of that percentage/class.
