Conformal Classification

Conformal classification is a statistical framework for building machine learning models that provide prediction sets with guaranteed coverage probabilities, offering reliable uncertainty quantification. Current research focuses on improving the efficiency and informativeness of these prediction sets by developing novel nonconformity scores and adapting methods to handle noisy labels, imbalanced datasets, and temporal dependencies in data, such as in time-series analysis. These advancements are significant because they enhance the trustworthiness and applicability of machine learning models in high-stakes domains requiring reliable uncertainty estimates, including healthcare, autonomous systems, and agricultural robotics.