Which of the following statements ARE CORRECT? a. Node impurity measures (such as Gini or Entropy) tend to prefer splits that result in large number of partitions. b. Information Gain is the expected reduction in entropy caused by partitioning the examples according to a given attribute. c. Entropy at a given note t, can have a minimum value of 0.5 - when all records belong to one class - implying most beneficial situation for classification. d. Classification error at a node t has a minimum value of 1-1/c when records are equally distributed among all classes.
Added by Amparo A.
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Node impurity measures prefer splits that result in a large reduction of impurity, not necessarily a large number of partitions. So, this statement is incorrect. b. Information Gain is indeed the expected reduction in entropy caused by partitioning the examples Show more…
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