I'm building a machine learning model that realizes sales predictions based on a set of features, but for this specific problem it would not be important to have a spot-on prediction.
The problem is that with the MSE loss function I'm getting some predictions that get spot-on predictions on part of the validation data, and gets a somewhat high error on other points.
So, I was thinking if there is a established function that would help the algorithm prioritize models without grotesque errors.
Thanks in advance.