Chapter 8 Tutorial.

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Presentation transcript:

Chapter 8 Tutorial

Expected information (entropy) needed to classify a tuple in D: Where pi is the probability that an arbitrary tuple in D belongs to class Ci, estimated by |Ci, D|/|D| Information needed (after using A to split D into v partitions) to classify D: Information gained by branching on attribute A

Question Consider the training examples shown in the table for a binary classification problem.

(A) what is the entropy of this collection of training examples with respect to the positive class? There are four positive examples and five negative examples. Thus, P(+) = 4/9 and P(−) = 5/9. The entropy of the training examples is −4/9 log2(4/9) − 5/9 log2(5/9) = 0.9911.

(b) What are the information gains of a1 and a2 relative to these training examples?

End