Probability Calibration

Probability calibration focuses on aligning the predicted probabilities of machine learning models with their actual accuracy, ensuring that a model's confidence reflects its true predictive ability. Current research emphasizes improving calibration in diverse applications, including image segmentation, drug discovery, and natural language processing, employing techniques like post-hoc calibration methods and Bayesian approaches alongside various model architectures (e.g., convolutional neural networks, transformers). Achieving well-calibrated models is crucial for reliable uncertainty quantification, facilitating informed decision-making in high-stakes domains and improving the trustworthiness of machine learning predictions.