What is machine learning uncertainty quantification?

Machine Learning Uncertainty Quantification (UQ) refers to the process of estimating and communicating the uncertainty in predictions made by machine learning models. This is important because it helps users understand how confident a model is in its outputs, which can guide decision-making in critical applications like healthcare, finance, and autonomous systems. UQ involves techniques such as probabilistic modeling, Bayesian inference, and ensemble methods to provide measures of confidence or probability along with predictions. Accurately quantifying uncertainty helps improve the reliability, safety, and interpretability of machine learning systems.