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Answer: Train a linear regression to predict a credit default risk score.
**Option B is correct** because: - Linear regression is well-suited for predicting continuous values like default risk scores - The dataset already contains labeled data (granted loans with default outcomes), which is ideal for supervised learning - Default rate prediction is essentially a regression problem where you want to predict the probability/risk score of default **Why other options are less appropriate:** - **A**: While more data can help, the immediate need is to build a predictive model with existing data - **C**: Removing bias is important, but collecting declined loan data introduces selection bias since those applications weren't granted - **D**: Social profile matching raises privacy concerns and may not be directly relevant to default prediction
Author: LeetQuiz .
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NO.40 You work for a bank. You have a labelled dataset that contains information on already granted loan applications and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants. What should you do?
A
Increase the size of the dataset by collecting additional data.
B
Train a linear regression to predict a credit default risk score.
C
Remove the bias from the data and collect applications that have been declined loans.
D
Match loan applicants with their social profiles to enable feature engineering.