Cross entropy

In information theory, the cross entropy between two probability distributions ${\displaystyle p}$ and ${\displaystyle q}$ over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set, if a coding scheme is used that is optimized for an "unnatural" probability distribution ${\displaystyle q}$, rather than the "true" distribution ${\displaystyle p}$.

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