Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective
This work introduces a family of generalized information measures based on the size and coverage of conformal prediction sets and formally relate conformal prediction to classical information-theoretic quantities and justifies using set-size reduction as an information gain metric.
Kevin B. Zhang, Stephen Bates
· 0 citations