Nevertheless, it is important to recognize that the distinction between aleatoric and epistemic uncertainty is context-dependent and can shift depending on the problem setup (X , Y, P, H) . For instance, replacing the instance space X with a more descriptive instance space X ′ (e.g., by adding features) can reduce aleatoric or model uncertainty (or both). However, this typically increases approximation uncertainty, as fitting a model with higher-dimensional features becomes more challenging with a fixed amount of data.