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:

Data From Problem 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.

Required

1. James wonders whether he should use new data to train a new model in December 2019, just prior to his planned investment on January 1, 2020. What should he do with the older data?
2. How much of the actual performance of the loans (such as repayments, loan restructuring, delayed repayments, hardship plans, etc.) should James use in classifying loans as default or repay when building  his

 prediction model?

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

Required

1. Write out the rules for this full decision tree.
2. Classify each loan in the validation sample as repay or default. Calculate the proportion of loans correctly classified as in Exhibit 11-13, column 5.

3. Based on his experience in management accounting, James reviews the model and notes that something appears to be wrong with the tree at a depth of 3. What problem did James observe? What should James propose?

Transcribed Image Text:

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