What is entropy decomposition in uncertainty-aware model evaluation?

Entropy decomposition separates uncertainty into aleatoric and epistemic components.

Total predictive uncertainty can be decomposed into aleatoric uncertainty (inherent data noise) and epistemic uncertainty (model uncertainty due to limited data). This decomposition is essential in safety-critical ML systems because epistemic uncertainty can be reduced with more data, while aleatoric cannot. Techniques like Bayesian neural networks or ensembles are used for estimation.