Predictive Probability

Predictive probability research focuses on accurately estimating the likelihood of different outcomes from a model's predictions, aiming to improve both model reliability and user trust. Current efforts concentrate on enhancing the precision of probability estimates, particularly for rare events and in high-stakes applications like autonomous systems and healthcare, employing techniques like multi-stage learning and novel ensemble methods such as single-branch trees. This work is crucial for building more robust and trustworthy AI systems, addressing issues of model calibration and uncertainty quantification, and enabling responsible deployment in various fields.