Expected Calibration Error

Expected Calibration Error (ECE) is a metric used to evaluate the calibration of probabilistic predictions made by machine learning models. Calibration refers to the agreement between predicted probabilities and actual outcomes; a well-calibrated model will produce probability estimates that reflect true likelihoods. ECE quantifies this by measuring the average difference between predicted probabilities and observed frequencies across multiple bins of predictions. Common use cases for ECE include assessing the reliability of models in classification tasks, particularly in fields like healthcare and finance where decision-making is critical. It helps in identifying models that may be overconfident or underconfident in their predictions, enabling practitioners to improve model performance and trustworthiness.