Refer to the previous exercise for a description of the problem and data set. Build a default

Question:

Refer to the previous exercise for a description of the problem and data set. Build a default classification tree to predict whether a customer will download the mobile banking app. Display the default classification tree.

a. How many leaf nodes are in the tree? What are the predictor variable and split value for the first split of the default classification tree? State the rule that can be derived from the first leaf node from the top of the tree diagram.

b. Build a full-grown tree. Which cp value is associated with the lowest cross-validation error?

c. Is there a simpler tree with a cross-validation error that is within one standard error of the minimum cross-validation error? If there is, then which cp value is associated with the best-pruned tree? How many splits are in 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. Create a confusion matrix and display the various performance measures. Assign Class 1 to be the positive class. What are the accuracy, sensitivity, specificity, and precision of the pruned tree on the validation data?

e. Generate the decile-wise lift chart. What is the lift value of the leftmost bar of the decile-wise lift chart?

f. Generate the ROC curve. What is the area under the ROC curve (or AUC value)?

g. Score the 20 new customers in the Mobile_Banking_ Score worksheet using the pruned tree. How many customers will likely download the mobile banking app based on your classification model? What is the probability of the first customer to download the mobile banking app? Round your answer to 4 decimal places.

Data from Exercises 17

Sunnyville Bank wants to identify customers who may be interested in its new mobile banking app. The worksheet called Mobile_Banking_Data contains 500 customer records collected from a previous marketing campaign for the bank’s mobile banking app. Each observation in the data set contains the customer’s age (Age), sex (Male/Female), education level (Edu, ranging from one to three), income (Income in $1,000s), whether the customer has a certificate of deposit account (CD), and whether the customer downloaded the mobile banking app (App equals 1 if downloaded, 0 otherwise). A portion of the data set is shown in the accompanying table. Create a classification tree model for predicting whether a customer will download the mobile banking app. Assign one as the success class as we are more interested in identifying customers who download the app. Select the best-pruned tree for scoring and display the full-grown, best-pruned, and minimum error trees.