This project forked from bambielli/informationgain
Calculate the InformationGain for a node in a decision tree
Calculate the information gained from a set of decisions.
This Class uses the concepts of information entropy (average amount of information encoded by a set of values) to determine which of a set of decisions provides the most information gain towards classifying new data in the problem space.
Information gain is useful when constructing decision trees, as it allows the designer to construct the most compact and information rich tree as possible.
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