Inductive Conformal

Inductive Conformal Prediction (ICP) is a machine learning technique that generates prediction sets, rather than single point predictions, providing guaranteed confidence levels for the predictions. Current research focuses on extending ICP's applicability to time series data and addressing challenges like concept drift and computational efficiency in high-dimensional settings, often employing ensemble methods and deep learning models such as deep regression forests and transformer networks. This robust approach to uncertainty quantification enhances the reliability and trustworthiness of machine learning models across diverse applications, from anomaly detection and biomedical data analysis to natural language processing and time-critical decision-making.