Google Professional Data Engineer
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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?
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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.
Explanation:
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
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