Calibration Statistic

Calibration statistics assess the reliability of uncertainty estimates produced by machine learning models, aiming to ensure that predicted uncertainties accurately reflect the true prediction errors. Current research focuses on improving the robustness of these statistics, particularly when dealing with heavy-tailed error distributions and exploring methods like the expected normalized calibration error (ENCE) and mean squared z-scores (ZMS). These advancements are crucial for building trustworthy machine learning systems across various applications, as reliable uncertainty quantification is essential for safe and effective deployment in high-stakes domains.