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Natural Language Processing Systems • Text Classification at ScaleMedium⏱️ ~3 min

Handling Class Imbalance and Long Tail Labels

The Core Problem
Class imbalance means some categories appear far more often than others. A support system might have 10,000 "billing" examples but only 50 "security breach" examples. The model learns to predict "billing" for everything because that minimizes training loss.
Why Standard Training Fails
Standard loss functions treat all examples equally. If 95% of training data is "billing," the model achieves 95% accuracy by always predicting billing. The 5% rare cases get ignored because missing them barely hurts the loss. For a security breach detector, this 5% is exactly what you need to catch.
Class Weighting
The simplest fix: weight each class inversely to its frequency. If "billing" has 10,000 examples and "security" has 50, give security examples 200x higher weight in the loss function. Now misclassifying one security example hurts as much as misclassifying 200 billing examples.
Set weight as: class_weight[i] = total_samples / (num_classes × class_count[i])
The Long Tail Problem
With 500+ categories, the tail gets extreme. Top 10 categories cover 80% of traffic. The remaining 490 share 20%, averaging 0.04% each. Even with class weighting, models struggle when a category has fewer than 50 training examples.
⚠️ Long Tail Fix: Group rare categories into an "Other" bucket during training. Use a second-stage classifier or zero shot model to handle Other with finer granularity at inference.
Alternative: hierarchical classification. First classify into 20 broad categories (enough examples each), then use category-specific models for fine-grained labels within each.

💡 Key Takeaways

✓Class imbalance causes models to predict majority class for everything, ignoring rare important cases

✓Class weighting: weight inversely to frequency so rare examples matter equally in loss

✓Long tail: with 500+ categories, most have fewer than 50 examples, too sparse for training

✓Group rare categories into Other bucket, use second-stage classifier for fine-grained

✓Hierarchical classification: broad categories first, then fine-grained within each

📌 Interview Tips

1Explain class weighting formula: weight = total_samples / (num_classes × class_count)

2For long tail, describe the Other bucket strategy with second-stage zero shot

3Mention hierarchical classification: broad categories → fine-grained within each

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