Assume the same information for Keebler-Olson as in Problems 11-31, 11-32, and 11-33. While James is presenting his work to the president and his team, someone asks whether he had calculated the overall likelihood value for the trees. Wanting to make sure that he had fully evaluated the model, James agrees that this is a good idea.

Data from Problems 11-31:

James Silva is a management accountant at Keebler-Olson, where he is in charge of their investment portfolio. In 2015, James worked with a data scientist to develop a model that predicts how a given loan will perform in the future based on the characteristics of the borrower available on the peer-to-peer lending platform Mandel Credit. On April 1, 2016, he purchased $100,000 worth of loans with 36-month terms (3 years). His investments had performed well. James planned to invest another $100,000 on January 1, 2020. Looking ahead, he considers some strategic questions around the model.

Data from Problem 11-32:

Assume the same information for Keebler-Olson as in Problem 11-31. James Silva  and the data scientist on his team work together to develop the following decision tree:

The data science team tests the model on the following validation set:

Required:

1. For the full and pruned trees, calculate likelihood values for the validation set in Problem 11-32 as in Exhibit 11-14, columns 6 and 8.
2. Calculate the overall likelihood value for the full and pruned trees.

Data from Exhibit 11-14:

3. As the management accountant, which model would you use for decision making based on the likelihood values? Does this differ from your answer to Problem 11-33, requirement 2? Explain.

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Depth 1 Depth 2 Depth 3 Default ($70,000) CUT 2 Income 8 1 <=675 CUT 1 Credit Score Repay 2 Repay 8 <= ($55,000) CUT 3 Income 1 X= $60,000) CUT 2 Income Default Repay