The kernel parameters, alongside noise variables, are typically subject to optimization to enhance model performance, employing specific optimization algorithms to achieve this objective. In this study, the GPy toolkit 60 is employed for the training and prediction across various datasets, illustrating the utility and adaptability of GPR in the context of UQ in polymer informatics. Despite their robust theoretical foundations, GPR models often incur high computational costs, limiting their practical applicability and exhibiting poor scalability with large training sets. 61 2.4. Metrics. UQ serves as a critical tool for evaluating the confidence of ML models in their predictions. UQ goes beyond traditional accuracy metrics to provide insights into how much trust can be placed in the model's outputs, which is essential for decision-making across diverse fields, including but not limited to polymer informatics. Despite its importance, assessing the quality of UQ remains challenging due to the absence of universally accepted standards and the contextspecific nature of uncertainty. In this study, we employ three independent metrics, including R 2 , Spearman's rank correlation coefficient and calibration area, to rigorously evaluate both the accuracy of predictions and the reliability of uncertainty estimates. To provide additional context, MAE and RMSE are also used alongside R 2 to quantify prediction performance, which are widely accepted in ML literature.