Exam Professional Data Engineer topic 1 question 91 discussion

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

We 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

"social" does not mean Social Media. This could be linked to demograhic data, si it could improve the score.

To predict default rates for credit applicants using the labeled dataset of granted loan applications, the most appropriate course of action would be: B. Train a linear regression to predict a credit default risk score. Here's the rationale for this approach: Appropriate Model for Prediction: Linear regression is a common statistical method used for predictive modeling, particularly when the outcome variable (in this case, the likelihood of default) is continuous. In the context of credit scoring, linear regression can be used to predict a risk score that represents the probability of default. Utilization of Labeled Data: Since you already have a labeled dataset containing information on loans that have been granted and whether they have defaulted, you can use this data to train the regression model. This historical data provides the model with examples of borrower characteristics and their corresponding default outcomes.

B. Train a linear regression to predict a credit default risk score.

What would be the target variable if B is correct i.e. training a linear regression model? Default/No-Default is a categorical variable one cannot train a linear regression model with this target variable

C - it is a typical approach in credit loans. Keeping only the accepted loans leads to a bias in the application

Linear regression is not the good way to solve such a problem, but you can totally apply linear regression to solve a classification problem. Just set the labels to numeric values 0 and 1 and linear regression will try to predict a value inbetween and round to the closest label (0 or 1). Totally not the way to go about it, but actually it's possible.

Cannot be B. This is logistic regression, not linear regression. D is the only acceptable option. Social profile can include things like high or low income, for example. When you apply for a credit you usually have to give this information, so totally legal.

Because there is no option to know what dataset schema is even though B is needed for this question's purpose Nobody can't select B. So there is none to answer

all options are wrong: Still in favour of B A: ofc its good to have more data but its not clear how much data we have B: Linear can be a workable approach but current situation is not for linear approach, decision tree, random forest etc can be good for it. C: DAta should be unbiased, removing bias is negative for tranining

default rates can be predicted with linear regression.

Answer should actually be a logistic Regression model

B is the answer.

The question asks about default RATES, as in you are predicting a continuous variable, not a discrete one (classification). This is a regression problem, so choice B.

I used to be a Credit Risk modeler and I think this question is stupid.

Data that you have is binary - Defaulted or not. You want default rates - Linear Regression. HOWEVER. The data that you have is for "ALREADY GRANTED" loan applications and whether they have defaulted or not. But you want to "train a model to predict default rates for credit applicants", which would include applicants who would not be granted the loan. If you just work on that dataset your model will not be as accurate as it wont have considered profiles of applicants that normally would not be granted those loans in the first place. Am I missing something here?