Refer to the previous exercise for a description of the data set. Build a default regression tree

Question:

Refer to the previous exercise for a description of the data set. Build a default regression tree to predict the customer’s annual household spending on travel products (TravelSpend). Display the regression tree.

a. How many leaf nodes are in the default tree? What are the predictor variable and split value for the first split of the default regression tree?

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

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 two new customers in the Travel_Score worksheet using the pruned tree. What are their predicted annual spending amounts on travel products?

Data from Exercises 34

Jerry Stevenson is the manager of a travel agency. He wants to build a model that can predict customers’ annual spending on travel products. He has compiled a data set that contains the following variables: whether the individual has a college degree (College), whether the individual has credit card debt (CreditCard), annual household spending on food (FoodSpend), annual income (Income), and annual household spending on travel products (TravelSpend). A portion of the Travel_Data worksheet is shown in the accompanying table. Create a regression tree model for predicting the customer’s annual household spending on travel products (TravelSpend). Select the best-pruned tree for scoring and display the full-grown, best-pruned, and minimum error trees.