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A Bank Wants to Understand Better the Details of Customers

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A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below:
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Fit a classification tree using Loan Default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data, and set the Minimum #records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to 7. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift charts for both the validation data and test data. a. Why is partitioning with oversampling advised in this case? b. Interpret the set of rules implied by the best pruned tree that characterize loan defaulters. c. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data? d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Fit a classification tree using Loan Default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data, and set the Minimum #records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to 7. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift charts for both the validation data and test data. a. Why is partitioning with oversampling advised in this case? b. Interpret the set of rules implied by the best pruned tree that characterize loan defaulters. c. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data? d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Fit a classification tree using Loan Default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data, and set the Minimum #records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to 7. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift charts for both the validation data and test data. a. Why is partitioning with oversampling advised in this case? b. Interpret the set of rules implied by the best pruned tree that characterize loan defaulters. c. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data? d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Fit a classification tree using Loan Default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data, and set the Minimum #records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to 7. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift charts for both the validation data and test data. a. Why is partitioning with oversampling advised in this case? b. Interpret the set of rules implied by the best pruned tree that characterize loan defaulters. c. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data? d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.
A bank wants to understand better the details of customers who are likely to default the loan. In order to analyze this, the data from a random sample of 200 customers are given below: In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Fit a classification tree using Loan Default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data, and set the Minimum #records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to 7. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift charts for both the validation data and test data. a. Why is partitioning with oversampling advised in this case? b. Interpret the set of rules implied by the best pruned tree that characterize loan defaulters. c. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data? d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.
In XLMiner's Partition with Oversampling procedure, partition the data so there is 50 percent successes (Loan default) in the training set and 40 percent of the validation data is taken away as test data. Fit a classification tree using Loan Default as the output variable and all the other variables as input variables. In Step 2 of XLMiner's Classification Tree procedure, be sure to Normalize input data, and set the Minimum #records in a terminal node to 1. In Step 3 of XLMiner's Classification Tree procedure, set the maximum number of levels to 7. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift charts for both the validation data and test data.
a. Why is partitioning with oversampling advised in this case?
b. Interpret the set of rules implied by the best pruned tree that characterize loan defaulters.
c. For the default cutoff value of 0.5, what are the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data?
d. Examine the decile-wise lift chart for the best pruned tree on the test data. What is the first decile lift? Interpret this value.

Identify and differentiate between the key elements involved in scenario analysis, such as drivers, strategies, and tactics.
Describe the basic steps involved in conducting a scenario analysis.
Recognize the importance of considering multiple levels and perspectives in scenario development for comprehensive strategy formulation.
Grasp the utility of scenario analysis as a long-term strategizing tool over other forms of planning.

Definitions:

Employer Notification

The requirement for employers to inform their employees or governmental bodies about certain workplace changes, health and safety issues, or benefits entitlements.

Implied Contract

A contract created by the actions or gestures of the parties involved in the transaction.

Nondiscriminatory Manner

Acting or making decisions without bias, unfairness, or discrimination towards any individual or group.

Worker Adjustment and Retraining Notification

A U.S. labor law requiring most employers with 100 or more employees to provide 60-day advance notification of plant closings and mass layoffs.

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