Consider the following set of training examples. (a) Compute a two-level decision tree using the greedy approach
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(a) Compute a two-level decision tree using the greedy approach described in this chapter. Use the classification error rate as the criterion for splitting. What is the overall error rate of the induced tree?
(b) Repeat part (a) using X as the first splitting attribute and then choose the best remaining attribute for splitting at each of the two successor nodes. What is the error rate of the induced tree?
(c) Compare the results of parts (a) and (b). Comment on the suitability of the greedy heuristic used for splitting attribute selection.
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a Splitting Attribute at Level 1 To determine the test condition at the root node we need to compute the error rates for attributes X Y and Z For attr...View the full answer
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Related Book For 
Introduction to Data Mining
ISBN: 978-0321321367
1st edition
Authors: Pang Ning Tan, Michael Steinbach, Vipin Kumar
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