Can Conformal Prediction Take You from Accurate to Reliable Machine Learning?

Conformal prediction (CP) is a technique that enhances machine learning model reliability by moving beyond simple accuracy metrics. It quantifies uncertainty by generating a prediction interval around each individual prediction, which helps build stakeholder trust in model outputs. The method is model-agnostic and computationally efficient, using a dedicated calibration dataset to compute conformal scores that measure the error between predictions and true labels. These scores are then used to determine a quantile that defines the width of the prediction interval, providing a formal guarantee on the coverage rate for new data.