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Thanks Claude, As an example, a perfect image would be a square perfectly centered inside another square with 4 corners that are not rounded and are 90 degrees that would be a "5". A square with 1 rounded corner but the internal square perfectly centered would be a "4" and a square with 2 rounded corners would be a "3". Let's assume I already have a rating criteria and a training set that already divides images into the categories of 5,4,3,2,1 based on some objective criteria (whatever that may be doesnt really matter for this discussion). What i want to know is: Can (or should) I use CreateML to PREDICT what my picture will rate irregardless of the content compared to other pictures of squares that have already been classified as 5,4,3,2,1. I just want to know if the qualities of the "unseen to the model" image can be analyzed to predict if they're most like one of the known 5,4,3,2,1 images and then assign a likeliness that my new image is a 4 vs a 3, etc.