In information theory, the cross-entropy between two probability distributions and 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 used for the set is optimized for an estimated probability distribution , rather than the true distribution . Definition The cross-entropy of the distribution relative to a distribution over a given set is defined as follows: , where is the expected value operator with respect to the distribution . The definition may be formulated using the Kullback–Leibler divergence , divergence of from (also known as the relative entropy of with respect to ). where is the entropy of . For discrete probability distributions and with the same support this means The situation for continuous distributions is analogous. We have to assume that and are absolutely continuous with respect to some reference measure (usually is a Lebesgue measure on a Borel σ-algebra). Let and be probability density functions of and with respect to . Then and therefore NB: The notation is also used for a different concept, the joint entropy of and . Motivation In information theory, the Kraft–McMillan theorem establishes that any directly decodable coding scheme for coding a message to identify one value out of a set of possibilities can be seen as representing an implicit probability distribution over , where is the length of the code for in bits. Therefore, cross-entropy can be interpreted as the expected message-length per datum when a wrong distribution is assumed while the data actually follows a distribution . That is why the expectation is taken over the true probability distribution and not . Indeed the expected message-length under the true distribution is Estimation There are many situations where cross-entropy needs to be measured but the distribution of is unknown. An example is language modeling, where a model is created based on a training set , and then its cross-entropy is measured on a test set to assess how accurate the model is in predicting the test data. In this example, is the true distribution of words in any corpus, and is the distribution of words as predicted by the model. Since the true distribution is unknown, cross-entropy cannot be directly calculated. In these cases, an estimate of cross-entropy is calculated using the following formula: where is the size of the test set, and is the probability