Say you're given a dataset where the response $y$ is continuous. The only prediction you're after is whether $y \geq C$, i.e., where the response is greater than some value.
In this case, would it be better to
(1) Use a regression algorithm that produces a continuous output, and then manually check if the response is $\geq C$?
(2) Use a classification algorithm. To do this, we must first preprocess the training data so the samples with $y_i \geq C$ are set to $1$ for binary classification and 0 otherwise.
It seems that both are feasible, but which is better, or what are the advantages/disadvantages of both?