Refer to the previous exercise for a description of the data set. Build a default regression tree to predict the customer’s spending during the first three months of the year (Spending). Display the regression tree. 

a. What are the rules that can be derived from the default regression tree? 

b. Build a full-grown tree. Which cp value is associated with the lowest cross-validation error? How many leaf nodes are in the minimum-error tree? 

c. Is there a simpler tree with a cross-validation error that is within one standard error of the minimum error? If there is, then which cp value is associated with the best-pruned tree? 

d. Prune the full tree to the best-pruned tree or the minimum error tree if the answer to part c is “No.” Display the tree. How many leaf nodes are in the pruned tree? 

e. What are the ME, RMSE, MAE, MPE, and MAPE of the pruned tree on the validation data? 

f. Score the 10 new customers in the E-Retailer_Score worksheet using the pruned tree. What are the mean and median values of the predicted spending amounts during the first three months?

Data from Exercises 38

An online retail company is trying to predict customer spending in the first three months of the year. Brian Duffy, the marketing analyst of the company, has compiled a data set on 200 existing customers that includes sex (Female: 1 = Female, 0 otherwise), annual income in 1,000s (Income), age (Age, in years), and total spending in the first three months of the year (Spending). A portion of the E-Retailer_Data worksheet is shown in the accompanying table. Create a regression tree model for predicting customer spending during the first three months of the year (Spending). Select the best-pruned tree for scoring and display the full-grown, best-pruned, and minimum error trees.

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Female Income 0 87.5 1 66.5 ⠀ ⠀ 0 51.9 Age 52 43 B 61 Spending 156.88 275.16 ⠀ 159.51