Macro-uniformity is an important factor in the overall quality of prints from inkjet printers. The International Committee for Information Technology Standards (INCITS) defined the macrouniformity for prints, which includes several printing defects such as banding, streaks, mottle, etc. Although we can quantitatively analyze a certain kind of defect, it is difficult to assess the overall perceptual quality when multiple defects appear simultaneously in a print. We used the Macro-uniformity quality rulers designed by INCITS W1.1 as experimental references, to conduct a psychophysical experiment for pooling perceptual assessments of our print samples from subjects. Then, calculated features can describe the severity of defects in a test sample; and we trained a predictive model using these data. The predictor can automatically predict the macro-uniformity score as judged by humans. Our results show that the predictor can work accurately. The predicted scores are similar to the subjective visual scores (ground-truth). Also, we used 6-fold cross-validation to confirm the efficacy of our predictor.