To further evaluate the robustness of our high-throughput analysis method under diverse training conditions, we combined all four classes of diblock copolymers and implemented a 5-fold cross-validation strategy. The dataset was randomly divided into five folds, with each iteration using four-folds for training and one-fold for testing. Employing the PIMF-RF approach results in 25 misclassified morphologies out of 364. Upon careful review, we found three block copolymers initially mislabeled during manual morphology assignment were identified and correctly predicted by our approach. These three SAXS patterns are shown in the SI. Figure (a) displays the maximum predicted probabilities for all morphologies, with mislabeled materials highlighted in red. We retrained our model of the revised dataset after correcting these labels, and the number of misclassified morphologies using our PIMF-RF method reduced to 20, which is much smaller than the 62 misclassifications observed with the CD-RF approach and 85 misclassified samples in Curve-RF approach using the same training data. Detailed results are provided in the SI.