Originally published at han-co.com · Part of the "Basics" strand of my Credit & Finance Data Science series. (The original has hand-drawn diagrams.)

This is the last piece.

So far we've built a good model (Parts 1 to 4), measured it honestly (Part 5), designed interventions with causation (Part 6), validated it and kept it alive (Parts 7 and 8), and laid the plumbing that makes it actually run (Part 9).

And yet, after all of that, nothing may happen. A model touches reality only when it becomes someone's decision. The score has to become an approval, a rate, a credit limit. And you have to bring people in other departments along with you.

This piece is about turning a score into a decision, and about conveying the result.

Set the cutoff with expected profit, not accuracy

The first question you hit after building a model is: from what score do we approve?

A common mistake is to settle this with a model metric — picking the point with the highest accuracy, or where F1 peaks.…