Re-Sampling Strategies are data-level techniques for handling class imbalance by modifying the training data distribution — either duplicating minority samples (over-sampling) or reducing majority samples (under-sampling) to create a more balanced training set.

Re-Sampling Methods

• Random Over-Sampling: Duplicate minority class samples randomly until balanced.
• Random Under-Sampling: Randomly remove majority class samples until balanced.
• SMOTE: Generate synthetic minority samples by interpolating between existing minority examples.
• Hybrid: Combine over-sampling of minority with under-sampling of majority.

Why It Matters

• Simplicity: Re-sampling is implemented at the data loader level — no model or loss modification needed.
• Risk: Over-sampling can cause overfitting on minority examples; under-sampling loses majority information.
• Effective: Despite simplicity, re-sampling remains one of the most effective strategies for imbalanced data.

Re-Sampling is balancing the data itself — modifying the training data distribution to give equal learning opportunity to all classes.