Data Science novice here!
I'm trying to work on the white/red wine quality data set, where I 'm trying to predict the quality of the wine. All the features are numerical.
The response variable however, is ordinal with a quality score of integers 1 through 10 . I have seen tutorials try to group the scores ie: (0-4: Bad, 5-7: Good, 8-10: Great), but what if I wanted to predict the score as it is?
Should I use a regression approach where I try and minimize the error of my predicted scores versus actual scores?
Or should I use a classification model anyways and instead of calculating a F-score to evaluate the model, find the model that minimizes a cost function?
Or perhaps there is another approach that works best?