Fraud detection with cost-sensitive machine learning

I quite enjoyed this—accuracy (as measured by F1 score) is not the only measure of success of an algorithm because not all errors have the same cost. This post walks through creating optimizing different types of models to minimize actual costs incurred rather than to maximize accuracy.

It's a long-ish post; if time is tight, just read the conclusion and the graphs at the end. Good takeaways.


Want to receive more content like this in your inbox?