Explanation:
The correct approach to predicting default rates for credit applicants using a labeled dataset that indicates whether loan applications have been defaulted is to use a linear regression to predict a credit default risk score. While linear regression is not typically used for binary classification problems, in this context, predicting a risk score can be seen as assigning a continuous value representing the likelihood of default. However, it is important to note that in practice, logistic regression is often preferred for binary outcomes.
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You work for a bank, and part of your responsibility is to manage and optimize the loan approval process. You have a labeled dataset at your disposal which includes detailed information on previously granted loan applications and whether these applications have subsequently defaulted. Your task is to use this dataset to train a predictive model that can estimate the likelihood of default for new credit applicants. What steps should you take to accomplish this task?
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.