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 "close to correct is better than far correct". 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?

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 "close to correct is better than far correct". 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.