Conformal prediction has gained increasing attention in recent years with the rapid development of machine learning.Known for its flexible structure and strict finite-sample theoretical guarantees,conformal prediction can be quickly and conveniently embedded into almost any prediction model.It performs rigorous uncertainty quantification by expanding prediction points into prediction sets.In this paper,we summarize the development history related to conformal prediction and review the basic algorithms and generalizations of conformal prediction along with the ubiquitous application scenarios of conformal prediction.
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