In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable Y {\displaystyle Y} given that the value of another random variable X {\displaystyle X} is known. Here, information is measured in shannons, nats, or hartleys.
In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable Y {\displaystyle Y} given that the value of another random variable X {\displaystyle X} is known. Here, information is measured in shannons, nats, or hartleys.
Source: Wikipedia "Conditional entropy" · CC BY-SA 4.0
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