Later, binary PNCs and pristine PLAs were printed using a commercial pellet printer by varying the temperature and flow rate, resulting in 186 independent printings. The printability and printing quality were assessed by measuring three physical properties: the weight fluctuation of the printed cylinders to determine whether they were over-or under-extruded, and the interior diameter and surface uniformity of the cylinders using digital image analysis. The relationship between material characteristics (thermal and rheological), printing parameters (temperature and flow), and printing quality first was analyzed by checking correlations via the Pearson correlation coefficient. Subsequently, a random forest algorithm was used to predict printing quality. The feature importance analysis was used to identify which material properties had the greatest impact on printing quality.