Classification and Regression Trees
Classification and Regression Trees
Chapter 6, page 127, problems 5-10.
| Occupation | Gender | Age | Salary |
| Service | Female | 45 | $48,000 |
| Male | 25 | $25,000 | |
| Male | 33 | $35,000 | |
| Management | Male | 25 | $45,000 |
| Female | 35 | $65,000 | |
| Male | 26 | $45,000 | |
| Female | 45 | $70,000 | |
| Sales | Female | 40 | $50,000 |
| Male | 30 | $40,000 | |
| Staff | Female | 50 | $40,000 |
| Male | 25 | $25,000 |
Consider the data in above Table. The target variable is salary. Start by discretizing salary as follows:
Less than $35,000 Level 1
$35,000 to less than $45,000 Level 2
$45,000 to less than $55,000 Level 3
Above $55,000 Level 4
5. Construct a classification and regression tree to classify salary based on the other variables. Do as much as you can by hand, before turning to the software.
6. Construct a C4.5 decision tree to classify salary based on the other variables. Do as much as you can by hand, before turning to the software.
7. Compare the two decision trees and discuss the benefits and drawbacks of each.
8. Generate the full set of decision rules for the CART decision tree.
9. Generate the full set of decision rules for the C4.5 decision tree.
10. Compare the two sets of decision rules and discuss the benefits and drawbacks of each.
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