Expected Calibration Error Trend 2026

Expected calibration error measures how closely a model's predicted confidence matches actual accuracy. Used in machine learning evaluation, it flags overconfident or underconfident predictions, guiding recalibration. Benefiting data scientists, ML engineers, and risk-sensitive fields like healthcare and finance, it ensures trustworthy probability estimates for better decision-making.