-) 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? p) 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? =) Compare the results of parts (a) and (b). Comment on the suitability of the greedy heuristic used for splitting attribute selection.
-) 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? p) 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? =) Compare the results of parts (a) and (b). Comment on the suitability of the greedy heuristic used for splitting attribute selection.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
Transcribed Image Text:No. of Class Cl Examples No. of Class C2 Examples
XY
000
40
15
10
45
10
25
11
|111
20
15
(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?
(e) Compare the results of parts (a) and (b). Comment on the suitability of
the greedy heuristic used for splitting attribute selection.
Transcribed Image Text:Chapter 3 Classification
7. Consider the following set of training examples.
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