Additionally, GPR permits the incorporation of noise into the data or kernel function, a mechanism that serves to refine the precision of uncertainty estimates. 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 57 has been 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.