ML Model Metric Credibility
How confident are you that your model performance isn't below 0.75 AUC? A framework for honest evaluation.
A single AUC number on a test set tells you almost nothing about the reliability of your model. What you need is a confidence interval — and the discipline to act on it.
Point Estimates Are Dangerous
Your model scored 0.82 AUC on the test set. Great. But what’s the 95% confidence interval? If it’s [0.71, 0.93], you might have a model that’s below your minimum threshold of 0.75.
Bootstrap Your Way to Honesty
Resample your test set with replacement 1,000 times. Compute AUC on each bootstrap sample. The 2.5th and 97.5th percentiles give you a confidence interval that accounts for the variance in your test data.
This is table stakes for any serious ML deployment. If your stakeholders are making decisions based on a single number, they deserve to know how much that number could vary.