serious model issue
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Accuracy masks class imbalance, confidence collapse, and user impact.
A model can maintain accuracy while becoming overly uncertain or biased toward majority classes. Secondary metrics reveal these issues earlier.
Track precision, recall, calibration, and input drift alongside accuracy.
Common mistakes:
Single-metric dashboards
Ignoring prediction confidence
No slice-based evaluation
Good monitoring is multi-dimensional.