Split Conformal Prediction
Finite-sample corrected predictive intervals from held-out residual ranks.
Free theory articleFinite-sample corrected predictive intervals from held-out residual ranks.
Concept & interpretation
Split Conformal Prediction is a distribution-free method that takes an existing, fixed prediction model and endows it with valid, finite-sample marginal coverage guarantees. It works by computing absolute residual scores on a held-out calibration set and using their order statistics to construct prediction intervals for new test points, relying only on the exchangeability of the data.
Interactive Experiment
Use the Split Conformal Prediction module to explore how the interval changes with different parameters.
- Set "Miscoverage alpha (%)" to 20.
- Set "Prediction center" to 1.
- Set "Calibration scores" to "Distinct".
- Observe that the calculated rank k is 8, the calibration radius is 0.800000, and the interval is [0.200000, 1.800000].
- Change "Miscoverage alpha (%)" to 20 and "Calibration scores" to "Tied". The rank stays 8, but its tied score is 1/5, giving interval [4/5, 6/5] and full width 2/5.
- Change "Miscoverage alpha (%)" to 5. The required rank k becomes 10. Since n=9, k > n, and the interval becomes unbounded ("all real numbers").