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Information gain ratio : ウィキペディア英語版
Information gain ratio

In decision tree learning, Information gain ratio is a ratio of information gain to the intrinsic information. It is used to reduce a bias towards multi-valued attributes by taking the number and size of branches into account when choosing
an attribute.〔http://www.ke.tu-darmstadt.de/lehre/archiv/ws0809/mldm/dt.pdf〕
== Information Gain Calculation ==

Let Attr be the set of all attributes and Ex the set of all training examples,
value(x,a) with
x\in Ex defines the value of a specific example x for attribute a\in Attr, H specifies the entropy. The values(a) function denotes set of all possible values of attribute a \in Attr.
The information gain for an attribute a\in Attr is defined as follows:
IG(Ex,a)=H(Ex) -\sum_ \left(\frac \cdot H(\)\right)
The information gain is equal to the total entropy for an attribute if for each of the attribute values a unique classification can be made for the result attribute. In this case the relative entropies subtracted from the total entropy are 0.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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