How to Teach Machine Learning Models a Sense of Uncertainty

Conformal prediction is a machine learning framework designed to quantify uncertainty in model outputs. Instead of making a single prediction, it provides a range of likely outcomes along with measures of confidence and credibility. Its agnostic nature means it can work with any algorithm and applies to both classification and regression tasks. Mondrian conformal prediction extends this framework to handle imbalanced datasets by ensuring equal error rates across classes. Together, these methods make AI predictions more interpretable, transparent, and reliable for real-world, high-risk applications.