Is there any reason not to use classification instead of regression when looking for ranges?
The general answer is how the model will be used deal. Either way may be optimal for the case.
For example - If the model groups applicants into good credit risk and bad credit risk, that might be fine to say model score &gt; x = good risk and model score &lt;= x = bad risk. But maybe there will be differential action based on the model score - like giving a different interest rate or a bigger loan.
In the original example, in regression actual = 191, predicted = 189 you can calculate the loss.
In classification, if actual = 191 and P(&gt;190)=0.35, P(170-190)=0.40, P(150-170)=0.25, then you just know the wrong class. Is that enough for the model usage?
There is also the assumption that a &quot;closer&quot; class will be chosen but that might not be true, e.g actual=191, P(&gt;190)=0.25, P(170-190)=0.25, P(150-170)=0.5. The regression could come up with 160 also but you can measure that loss if the model usage requires it. Many classification algorithms do not know if classes are &quot;close&quot; - Confusion matrix. &quot;How close I am to the diagonal?&quot;. Is there such metric?
You can also look at Ordinal Regression https://en.wikipedia.org/wiki/Ordinal_regression. In this case there is an implicit ranking in the &quot;class&quot;.
Choose based on how the model will be used. Always important to know the usage and the problem being solved, then work backwards to the model.
Hope that helps.