James Silva is a management accountant at Keebler minus Olson, where he is in charge of their investment portfolio. 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. James Silva and the data scientist on his team work together to develop the following decision tree: Start by classifying each loan in the validation sample as (0) Repay or (1) Default using the pruned tree. Observation Income Credit Score Actual Outcome Model Prediction (Pruned Tree) (1) (2) (3) (4) (5) 1 $85,000 710 (0) Repay 2 $62,000 650 (1) Default 3 $72,000 660 (0) Repay 4 $75,000 640 (0) Repay 5 $71,000 680 (0) Repay 6 $59,000 705 (0) Repay 7 $48,000 690 (1) Default 8 $57,000 685 (0) Repay

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.

2. Based on your answer to requirement 1 and the results from validation using the full tree, 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 minus 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?