I have developed several models to predict a given output (let's say the output is something like "test score", that goes from 0% to 100%), based on several variables that influence that result
Since I'm not interested in predicting the actual precise score, but only in predicting the range in which it will be, and specially because my ML knowledge is 100% self learned and I had a very limited time to learn it, and so I wanted to minimize the number of models I had to learn (and I had already learned the classification NN algorithm), I have divided the outputs in classes according to range (for example, A would be 80-100%, B would be 60-80%, etc etc). Therefore, I used the Neural Network as a classification algorithm.
From a theoretical point of view, would there be any advantage in using the NN as a regression algorithm to predict an actual value instead of class?