My questions is as above. What are the most important matrices (f1, precision, recall...etc) that I need to prioritize my work to improve for evaluating how good a model predict customer churn and the reason behind it.

For example: a fraud detection data product that had a business constraint: zero false positives. I set the constraint myself.

The reason for it is that we ban users based on that data product’s output. It is absolutely inadmissible that a user is banned incorrectly based on the output of an algorithm.

In this specific case I prefer to have dozens, even hundreds of false negatives than one single false positive.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.