Refer to the scenario described in Problem 10 and the file BlueOrRed. Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Fit a classification tree using Age, HomeOwner, Female, Married, HouseholdSize, Income, and Education as input variables and Undecided as the output variable. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data and to set the Minimum
#records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to seven. Generate the Full tree, Best pruned tree, and
Minimum error tree. Generate lift charts for both the validation data and the test data.
a. Interpret the set of rules implied by the best pruned tree that characterize undecided voters.
b. In the CT_Output1 sheet, why is the overall error rate of the full tree 0 percent? Explain why this is not necessarily an indication that the full tree should be used to classify future observations and the role of the best pruned tree.
c. For the default cutoff value of 0.5, what is the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data?
d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.

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