Why is accuracy a bad primary metric for many real-world problems?
easyAnswer
- Accuracy weights every error equally and ignores class balance.
- On a 99% negative dataset, always predicting negative gives 99% accuracy — a model that's completely useless.
- Even on balanced problems it hides asymmetric costs (missed fraud is worse than a false alarm, or vice versa).
- Prefer metrics matched to the deployment cost: precision at fixed recall, F1, PR-AUC, expected cost, Brier score.
- Always report accuracy alongside the class prior so readers can spot base-rate issues.
Check yourself — multiple choice
- It handles class imbalance correctly
- It ignores class balance and asymmetric error costs — always report the base rate too
- It only fails for regression
- It's always the best metric
Accuracy ignores class balance and cost asymmetry — misleading on imbalanced problems.
#metrics#classification#imbalance
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