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Information gain measures the expected reduction in entropy caused by partitioning the examples according to an attribute:
First term: entropy of the original collection S; second term: expected value of entropy after S is partitioned using attribute A (Sv subset of S).
Gain(S,A): The expected reduction in entropy caused by knowing the value of attribute A.
ID3 uses information gain to select the best attribute at each step in growing the tree.
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