Calibration Metric

Calibration metrics assess the reliability of probabilistic predictions from machine learning models, ensuring that predicted probabilities accurately reflect the likelihood of the predicted outcome. Current research focuses on improving the reliability and applicability of these metrics across various model architectures (including deep neural networks and tree-based models) and tasks, addressing limitations of existing metrics like Expected Calibration Error (ECE) and exploring alternatives such as interval-based metrics and those based on distribution matching or statistical testing. This work is crucial for building trustworthy AI systems, particularly in high-stakes applications like medical diagnosis and autonomous systems, where accurate uncertainty quantification is paramount.