I am confused about these models In the first problem, it's been told to accept the maximum number of good customers, if at least 98% of the customers that are do not repay their debt correctly identified.
I am confused about what is meant by this? Do I need to train the models and set the threshold value accordingly so that I may obtain 98% accuracy? And for the second one, I need to decrease the threshold value so that accuracy comes down to 85%?
I am stuck in this for a while. Please help me show the correct path. Thanks.
data: German Credit Risk data.
Variables: Several independent variables with the dependent variable &quot;Credit_Risk&quot; which responses in &quot;Good&quot; and &quot;Bad&quot;.
The goal is to identify at least 98% of the customers, that do not repay their debt. So the bank can &quot;accept a maximum number of 'good' customers, that can be granted loans&quot; Here the goal is focused on the bad customers.

There should be at least 85% good customers accepted while the side focus is to reject as many bad customers as possible.


I think the difference between 1) and 2) is identifying bad customers in 1) and good customers in 2)