Conformal Prediction
A mathematically rigorous framework that wraps any existing machine learning model to output statistically guaranteed prediction intervals rather than points.
Think of It Like This
Like a weather forecaster saying 'It will be between 65 and 75 degrees' rather than just guessing exactly 70 degrees, guaranteeing they are rarely wrong.
Instead of a neural network simply predicting 'Cat' with an uncalibrated 99% confidence score, conformal prediction uses a small calibration dataset to convert that output into a set. It guarantees, with a user-specified confidence level like 95%, that the true label is actually contained within the output set. This is a game-changer for deploying black-box models in high-risk medical or financial settings.