James Silva is a management accountant at Keebler - 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: (Click the icon to view the decision tree.) The data science team tested the full decision model on a validation set resulting in 5/8 correct classifications. (Click the icon to view the validation set using the full decision tree.) Read the requirements. Requirement 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. 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 $ EA 57,000 685 (0) Repay Ask my instructor Clear all Check answer on list K tion 3 stion 4 James Silva is a manag will perform in the future develop the following de (Click the icon to vi The data science team (Click the icon to vi Read the requirements. Figure Depth 1 (Click the icon to The data science tea Click the icon to Data table estion 3 Read the requireme O - X Model Prediction Observation estion 4 (1) Income (2) Credit Score Actual Outcome (Full Tree) (3) (4) (5) Requirement 1. Pru 1 $ 85,000 710 (0) Repay (0) Repay the probability of default is greater than 0.5 classify th estion 5 default). Calculate th 2 62,000 650 (1) Default (1) Default Start by classifying e 3 $ 72,000 660 (0) Repay (0) Repay 4 $ 75,000 640 (0) Repay (0) Repay uestion 6 Observation 5 $ 71,000 680 (0) Repay (0) Repay (1) 6 $ 59,000 705 (0) Repay (1) Default Question 7 1 $ 7 $ 48,000 690 (1) Default (0) Repay 2 69 8 57,000 685 (0) Repay (1) Default 3 Question 8 4 5 Question 9 78 EA 69 6 S 69 69 Print Done 57,000 685 (0) Repay science team tested the full < the icon to vi requirements. ment 1. Prune Calculate the p classifying eac vation In 1) 1 $ 8 2 3 69 S Requirements alidation in 5/0 rrent classificatione 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-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? (if the pro 4 5 6 $ Print Done 7 48,000 690 (1) Default 57.000 685 (0) Rena