Uncertainty Wrapper

Uncertainty wrappers are methods for quantifying the uncertainty associated with predictions from machine learning models, particularly crucial in safety-critical applications like medicine and autonomous systems. Current research focuses on improving the accuracy and interpretability of uncertainty estimates, often employing decision trees or ensembles like random forests to classify input data and assign uncertainty levels. This work aims to enhance the reliability and trustworthiness of AI systems by providing users with transparent and dependable measures of model uncertainty, thereby improving decision-making in various domains.