p/Chapter\%201 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 I preferred using models based on full decision trees because they seem to fit the training data more closely. How should James expli the pruned decision tree model? (1) (2) (0) Repay (1) Default (3) (0) Repay (1) Default (4) (0) Repay (1) Default (5) (0) Repay (1) Default (0) Repay (1) Default (6) (0) Repay (1) Default (7) (0) Repay (1) Default (8) (0) Repay (1) Default (9) (10) full pruned (11) higr lowe (12) the bias-variance tradeoff the concept of data leakage what the receiver operating characteristic (ROC) curve is (13) risks overfitting the training data, as the model may start fitting the noise of the data instead of the signal. shows that the cross-validation sample contained outliers. James Silva is a management accountant at Keebler - Olson, where he is in charge of their investment portfolio. James worked with 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 following decision tree: 1 (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. 2 (Click the icon to view the validation set using the full decision tree.) Read the requirements 3. Requirement 1. Prune the tree at depth 3. Using the pruned tree, classify each loan in the validation sample as repay or default (if th 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. The proportion of loans correctly classified is (9) Requirement 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? James should use the (10) the validation set. tree to identify default and repay loans as this model has a (11) accurac Requirement 3. James has to present both models and the conclusions to the president of Keebler-Olson. He knows that in the pas 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? p/Chapter\%201 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 I preferred using models based on full decision trees because they seem to fit the training data more closely. How should James expli the pruned decision tree model? (1) (2) (0) Repay (1) Default (3) (0) Repay (1) Default (4) (0) Repay (1) Default (5) (0) Repay (1) Default (0) Repay (1) Default (6) (0) Repay (1) Default (7) (0) Repay (1) Default (8) (0) Repay (1) Default (9) (10) full pruned (11) higr lowe (12) the bias-variance tradeoff the concept of data leakage what the receiver operating characteristic (ROC) curve is (13) risks overfitting the training data, as the model may start fitting the noise of the data instead of the signal. shows that the cross-validation sample contained outliers. James Silva is a management accountant at Keebler - Olson, where he is in charge of their investment portfolio. James worked with 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 following decision tree: 1 (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. 2 (Click the icon to view the validation set using the full decision tree.) Read the requirements 3. Requirement 1. Prune the tree at depth 3. Using the pruned tree, classify each loan in the validation sample as repay or default (if th 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. The proportion of loans correctly classified is (9) Requirement 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? James should use the (10) the validation set. tree to identify default and repay loans as this model has a (11) accurac Requirement 3. James has to present both models and the conclusions to the president of Keebler-Olson. He knows that in the pas 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