Question: Suppose that, at a node N of a decision tree, we have 1000 training examples. There are four possible class labels (A, B, C, D)

Suppose that, at a node N of a decision tree, we have 1000 training examples. There are four possible class labels (A, B, C, D) for each of these training examples. Part a: What is the highest possible and lowest possible entropy value at node N? Part b: Suppose that, at node N, we choose an attribute K. What is the highest possible and lowest possible information gain for that attribute?

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