Refer to scenario in Problem 8 using the le Cellphone. Using XLMiner's Partition with Oversampling procedure, partition the data with all the variables so there is 50% successes (churners) in the training set and 40% of the validation data are taken away as test set. Use 12345 as the seed in the randomized sampling. Fit a single classification tree using Churn as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classication Tree procedure, be sure to Normalize Input Data and to set the Minimum # records in a terminal node to 1. Generate the Full tree, Best pruned tree, and Minimum error tree.

a. Why is partitioning with oversampling advised in this case?

b. From the CT_Output worksheet, what is the overall error rate of the full tree on the training set? 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. Consider the minimum error tree in the CT_MinErrorTree worksheet. List and interpret the set of rules that characterize churners.

d. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best-pruned tree on the test set?

e. Examine the decile-wise lift chart for the best-pruned tree on the test set. What is the first decile lift? Interpret this value.