Why a 0.98 ROC-AUC can still ship a broken classifier
Article summary
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A model I was reviewing reported 0.98 ROC-AUC on a fraud dataset. Great number. Then it went live and roughly 95% of its alerts were false alarms. The metric wasn't wrong — it was answering a question nobody in production cared about. The plot that would have caught this up front is the precision-recall curve, so I built an interactive one you can poke at. Live, computed in your browser: https://dev48v.infy.uk/ml/day31-precision-recall-curve.html Drag a class-imbalance slider and watch the ROC…
1Key Takeaways
- A model I was reviewing reported 0.98 ROC-AUC on a fraud dataset.
- Then it went live and roughly 95% of its alerts were false alarms.
- The metric wasn't wrong — it was answering a question nobody in production cared about.
- The plot that would have caught this up front is the precision-recall curve, so I built an interactive one you can poke at.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a model I was reviewing reported 0.98 ROC-AUC on a fraud dataset.
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