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💻Interactive•25 min

Handling Data Imbalance

Master techniques for dealing with imbalanced datasets.

Handling Data Imbalance

The Problem

When classes have vastly different sample sizes:

  • Models bias toward majority class
  • Accuracy becomes misleading
  • Rare events get missed

    • Solutions

    1. Resampling

  • Oversampling: Duplicate minority samples (SMOTE)
  • Undersampling: Remove majority samples

    • 2. Class Weights Penalize misclassification of minority class more heavily.

    3. Focal Loss Down-weights easy examples, focuses on hard cases.

    4. Data Augmentation Create synthetic minority samples.

    Evaluation for Imbalanced Data

  • Use Precision, Recall, F1-Score
  • ROC-AUC and PR-AUC
  • Avoid relying on accuracy alone

    • 🎯 Key Takeaways

    • ✓Imbalanced data biases models toward majority class
    • ✓SMOTE creates synthetic minority samples
    • ✓Class weights can balance the loss function
    • ✓Use appropriate metrics like F1-Score

    📚 Additional Resources

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