Assume the same information for Keebler- Olson as in Problems 11-31 and 11-32.

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:

1. Prune the tree at depth 3. Using the pruned tree, classify each loan in the validation sample as repay or default (if the probability of default is greater than 0.5 classify the loan as default). Calculate the proportion of loans correctly classified as in Exhibit 11-13, column 6.

2. Based on your answer to requirement 1 of this problem and Problem 11-32, requirement 2, which decision tree should James use to identify default and repay loans?
3. James has to present both models and the conclusions to the president of Keebler-Olson. He knows that in the past the president has preferred using models based on full decision trees because they seem to fit the training data more closely. How should James explain the pruned decision tree model?

Transcribed Image Text:

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