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In information theory, perplexity is a measurement of how well a probability distribution or probability model predicts a sample. It may be used to compare probability models. == Perplexity of a probability distribution == The perplexity of a discrete probability distribution ''p'' is defined as : where ''H''(''p'') is the entropy of the distribution and ''x'' ranges over events. Perplexity of a random variable ''X'' may be defined as the perplexity of the distribution over its possible values ''x''. In the special case where ''p'' models a fair ''k''-sided die (a uniform distribution over ''k'' discrete events), its perplexity is ''k''. A random variable with perplexity ''k'' has the same uncertainty as a fair ''k''-sided die, and one is said to be "''k''-ways perplexed" about the value of the random variable. (Unless it is a fair ''k''-sided die, more than ''k'' values will be possible, but the overall uncertainty is no greater because some of these values will have probability greater than 1/''k'', decreasing the overall value while summing.) 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Perplexity」の詳細全文を読む スポンサード リンク
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