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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Uncertainty coefficient ： ウィキペディア英語版 Uncertainty coefficient In statistics, the uncertainty coefficient, also called proficiency, entropy coefficient or Theil's U, is a measure of nominal association. It was first introduced by Henri Theil and is based on the concept of information entropy. ==Definition== Suppose we have samples of two discrete random variables, ''X'' and ''Y''. By constructing the joint distribution, , from which we can calculate the conditional distributions, and , and calculating the various entropies, we can determine the degree of association between the two variables. The entropy of a single distribution is given as: 〔 〕 :$$ H(X)= -\sum_x P_X(x) \log P_X(x) , while the conditional entropy is given as:〔 :$$ H(X|Y) = -\sum_ P_(x,~y) \log P_(x|y) . The uncertainty coefficient〔 〕 or proficiency is defined as: :$$ U(X|Y) = \frac = \frac , and tells us: given ''Y'', what fraction of the bits of ''X'' can we predict? (The above expression makes clear that the uncertainty coefficient is a normalised mutual information ''I(X;Y)''.) In this case we can think of ''X'' as containing the "true" values. Note that the value of ''U'' (but not ''H''!) is independent of the base of the ''log'' since all logarithms are proportional. The uncertainty coefficient is useful for measuring the validity of a statistical classification algorithm and has the advantage over simpler accuracy measures such as precision and recall in that it is not affected by the relative fractions of the different classes, i.e., ''P''(''x'') .〔 〕 It also has the unique property that it won't penalize an algorithm for predicting the wrong classes, so long as it does so consistently (i.e., it simply rearranges the classes). This is useful in evaluating clustering algorithms since cluster labels typically have no particular ordering.〔 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「Uncertainty coefficient」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.