Conformal Prediction Coverage - Interactive Visualization

Conformal prediction is a framework for producing prediction sets - not just point predictions - with a statistically rigorous coverage guarantee. For a target coverage level of 90%, conformal prediction guarantees that the true label falls within the prediction set for at least 90% of new data points, regardless of the underlying data distribution. It requires only exchangeability, not model correctness.

• Set the target coverage level (e.g., 90%) and watch the conformal threshold computed from calibration nonconformity scores
• See prediction sets: instead of a single class, the model outputs a set of classes guaranteed to contain the true label
• Understand how prediction set size varies: easy examples get singleton sets, hard examples near boundaries get larger sets
• Learn split conformal: a calibration set is held out to compute the threshold - training is completely unmodified
• Compare conformal prediction to Bayesian credible intervals: conformal is distribution-free and does not require model calibration

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