Spearman's rank correlation coefficient is a statistical measure that evaluates the monotonic relationship between two variables. In the context of ML for uncertainty prediction, particularly in polymer informatics, this coefficient is utilized to analyze the correlation between actual errors and the associated uncertainties (standard deviations). Essentially, the model's goal is to achieve low prediction errors with correspondingly low uncertainties. To apply this method, two vectors are considered: one representing the absolute errors of predictions and the other representing the standard deviations indicative of uncertainty. Both vectors are ranked in ascending order based on their values, and these ranks are then used to calculate the Spearman's coefficient, and the formulation can be given by