Figure (b) presents the violin plot of the maximum predicted probabilities of all heldout test samples by the PIMF-RF method. Analysis shows that when using a threshold of 0.6 for the predicted probabilities, we need to examine only 32 SAXS patterns (9% of the data) to achieve a predictive accuracy of 98.4%. Increasing this threshold to 0.8 requires inspecting 69 samples (19% of the data), which results in remarkably accurate predictions with 100% accuracy. This pattern suggests that strategically examining a small subset of SAXS patterns with lower predicted probabilities enables the ML approach to attain high predictive accuracy.