Calculates the table with p-values for test data based on the provided calibration table. The p-value for a class is the fraction of entries from that class in the calibration set that have a model probability less than or equal to the model probability for the record under consideration. Small p-values indicate records that are nonconforming, larger p-values indicate records that are conforming. (Reference: Vovk, V., Gammerman, A. and Shafer, G., 2005. Algorithmic learning in a random world. Springer Science & Business Media.)
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