Conformal prediction (CP) is an algorithm for uncertainty quantification that produces statistically valid prediction regions (multidimensional prediction intervals) for any underlying point predictor (whether statistical, machine learning, or deep learning) only assuming exchangeability of the data. CP works by computing "nonconformity scores" on previously labeled data, and using these to create prediction sets on a new (unlabeled) test data point.
Conformal prediction (CP) is an algorithm for uncertainty quantification that produces statistically valid prediction regions (multidimensional prediction intervals) for any underlying point predictor (whether statistical, machine learning, or deep learning) only assuming exchangeability of the data. CP works by computing "nonconformity scores" on previously labeled data, and using these to create prediction sets on a new (unlabeled) test data point.
Source: Wikipedia "Conformal prediction" · CC BY-SA 4.0
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