I'm trying to create an image aesthetics rating system, but I realize most of the ratings are rather average, lying within the range of 50+. I'm using a classification output (instead of a regression one), where there are 100 possible classes, and each class representing the score.If the majority of my training examples have the score of 50+, does that mean for photos which are supposed to have higher ratings, their ratings will have lower predicted values? How could I minimize this impact for higher accuracy?
My ground truths output lie largely from the range 50+ to 100, but the possible scores are 1-100. How would this skew my model's accuracy?
machine learning;classification;accuracy