Topic 1 Question 91
You work for a bank. You have a labelled dataset that contains information on already granted loan application 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?
Increase the size of the dataset by collecting additional data.
Train a linear regression to predict a credit default risk score.
Remove the bias from the data and collect applications that have been declined loans.
Match loan applicants with their social profiles to enable feature engineering.
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コメント(17)
A is incorrect as you need to work with the data you have available C is an optimisation not a solution D is ethically incorrect and invasion to privacy, there could be several legal implications with this B although oversimplified but is a workable solution
👍 34GHN742020/09/01We have labelled data that contains whether a loan application is accepted or defaulted - So Classification Problem Data.
We need to predict (Default Rates for applicants) - I think whether application will be granted or defaulted. - So Binary Classification.
No option matches the answer. - if we mark 'B' - It should be Logistic Regression, Instead of Linear Regression
👍 16sumanshu2021/04/06I used to be a Credit Risk modeler and I think this question is stupid.
👍 8woyaolai2022/11/08
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