Class Imbalance
Class imbalance causes models to predict majority class and have poor performance on minority classes.
Class imbalance causes models to predict majority class and have poor performance on minority classes.
What this failure is
Class Imbalance is a Data Pipeline failure seen during ML training runs. Class imbalance causes models to predict majority class and have poor performance on minority classes. Common tags: Class Imbalance, Focal Loss, Oversampling, Long Tail.
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Why it happens (the mechanism)
Some classes have very few examples. Loss weights not adjusted. No oversampling/undersampling. Hard examples not weighted. Focal loss not used. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Model predicts majority class for all inputs
- Minority classes have very low recall
- Accuracy is high but per-class F1 is poor
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Confusion matrix shows bias to majority class | Some classes have very few examples |
| Loss is dominated by majority class | Loss weights not adjusted |
| Macro F1 is much lower than micro F1 | No oversampling/undersampling |
Which systems are affected
- Long-tail classification
- Medical diagnosis (rare diseases)
- Fraud detection (rare fraud)
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: Confusion matrix shows bias to majority class
- ✓Verified signal present: Loss is dominated by majority class
- ✓Verified signal present: Macro F1 is much lower than micro F1
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Diagnose this failure in VS Code
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Root cause
- Some classes have very few examples
- Loss weights not adjusted
- No oversampling/undersampling
- Hard examples not weighted
- Focal loss not used
The fix and how to prevent it
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