In the study of Marmoset auto-grading data, decision-tree classifiers predicted students as good, satisfactory, or poor performers, while linear-regression models predicted numeric exam grades. Classification models trained separately on the four feature categories performed poorly and assigned most students to the good-performance category; confusing poor performers with good performers was especially problematic. Regression performed substantially better, with a reported mean difference of 0.92 on a 120-point grading scale. Converting the predicted grades into performance categories also produced better results than direct classification. A separate regression model using only the interval between submission and deadline indicated a strong correlation between that feature and exam performance.