TelMark Mobile Services provides mobile phone services to millions of customers in the U.S. TelMark’s customer base is constantly changing as old customers leave the service and new customers join. This is called churn and it is a very important dynamic for the company to manage effectively because it has significant profit implications. Churn has increased recently, so managers at TelMark have started a program to offer targeted promotions to customers who are likely to abandon the service.
They would like to develop a machine learning model to help identify these customers. Sylvia Restler is the management accountant assigned to work on the project with the data science team.
Sylvia speaks with a few members of the customer service team to better understand why customers leave their platform. The team tells her that two variables are highly related to customer churn: number of lines per customer family plan and number of months the customer has been with the company. According to their data, customers who have already been with the company for a long time tend to stay, as do customers with many lines on their family plan. Sylvia asks the data science team to use these variables to create a decision tree.

The team uses the following as a validation set:

Required

1. Write out the rules for this decision tree.
2. Using the full decision tree, classify each customer in the validation sample as leave or stay. Calculate the proportion of customers correctly classified as in Exhibit 11-13, column 5.

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Depth 1 Depth 2 Depth 3 Leave Leave <= 1.5 CUT 1 Number of Lines <= 21.5 CUT 3 Months <= 3.5 CUT 2 Number of Lines Stay Stay