Distill-then-Detect: A Practical Framework for Error-Aware Machine Learning

Author(s): Shenggang Li

Originally published on Towards AI.

Leveraging Teacher Uncertainty, Student Distillation, and Conformal Calibration to Diagnose and Flag High-Risk PredictionsPhoto by Sigmund on Unsplash

Even the most advanced neural networks or boosting algorithms sometimes stumble on a small but critical slice of data — often around 10% of validation cases — where prediction errors blow up.

These “big misses” usually stem from messy real-world inputs: outliers, unusual feature combinations, or hidden patterns the model never learned. Without a way to pinpoint these tricky cases, businesses can make costly mistakes.

In credit scoring, for example, misclassifying just a handful of high-risk applicants can lead to major loan defaults. In manufacturing, failing to flag the few machines about to fail can halt entire production lines.

My solution stitches together three practical steps. First, I distill a compact “student” model from a powerful “teacher” to retain accuracy while boosting speed. Next, I quantify prediction uncertainty and train a lightweight meta-model to learn where the teacher tends to err. Finally, I apply a calibrated thresholding method that guarantees I catch most high-risk cases without swamping the team with false alarms.

By clustering the worst observations, I can also show actionable patterns — say, customers with extreme discount rates or machines operating under rare conditions.

The method not only improves overall accuracy but also equips decision-makers with a built-in radar… Read the full blog for free on Medium.

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Published via Towards AI