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IS 4487-006 Fall 2025 Final Exam December 10 from 10:30 to 12:30

Single choice

Your bank wants to build a system that automatically approves or denies small consumer loans. Analysts have a historical dataset with applicant age, income, marital status, and number of dependents, along with a labeled outcome (“good” vs. “bad” borrower). The team is considering using either a single decision tree or a random forest, and they show you the decision tree diagram below as an example of how the logic works. Which model should the bank choose if they want higher accuracy and more stability in predictions, especially when detecting risky or fraudulent applications?

Options
A.Logistic Regression
B.Random Forest
C.Single Decision Tree
D.K-Means Clustering
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To determine the best model for higher accuracy and stability in predicting risky or fraudulent loan applications, we need to compare the options based on how they handle real-world data and generalization. Option 1: Logistic Regression - This linear model assumes a linear relationship between features and the log-odds of the outcome. While fast and interpretable, it may underfit when the decision boundary is nonlinear or when interactions among feat......Login to view full explanation

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