Cast regression problem into classification problem In the TA session, my TA claimed, that regression problems should often be cast into classification problems by dividing the output range into bins and then using a multi-loss, since we have better classification than regression algorithms.
In my understanding, this is inherently wrong as it discards the property, that &quot;close to correct is better than far correct&quot;. All wrong classes are equally wrong.
I asked my professor, but he just said, there are applications where it makes sense and did not want to discuss it more.
Am I wrong? When should I cast a regression problem into a classification problem?
Edit: I do not know if my TA referred to it, but here is a tweet from A. Karpathy: https://twitter.com/karpathy/status/708480082831024128

not-widely-enough-known-protip: Do not use L2 loss (regression) in
neural nets unless you absolutely have to. Softmax likely to work
better.
The statement in principle seems very vague, how can one state that there are better classification than regression algorithms?
With that said I would rephrase the statement to:
Sometime it is feasible to turn a regression into a classification problem due to for the problem itself, it makes sense to predict a range/bin instead of a continuous value.
When doing this we have to be careful since we are not facing a &quot;common&quot; classification problem but we have an ordinal classification problem in which we have a natural order from the new target (bins)
For reference check:
https://stats.stackexchange.com/questions/493254/why-ordinal-target-in-classification-problems-needs-special-attention