Moreover, even within a fixed setup, separating predictive uncertainty into its noise, variance, and bias components is not always clear-cut and can often become blurred. For example, consider multiple instances with identical features but differing labels. In such cases, it can be challenging to discern whether the variation is due to true randomness (noise) or the modeler's lack of knowledge about the existing but unaccounted differences between these data points (bias). This overlap complicates the process of clearly identifying the sources of uncertainty.