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Fraudulent Loan Applications [Affirm Machine Learning Interview Question]


Assume a classifier that produces a score between 0 and 1 representing the probability of a particular loan application being fraudulent.

In this scenario, what do false positives and false negatives represent? What are the tradeoffs between them in terms of dollars, and how should the model be weighted accordingly?

This is the same question as problem #16 in the Machine Learning Chapter of Ace the Data Science Interview!