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Which technique handles imbalanced datasets by adjusting class weights during training?
- Oversampling
- Undersampling
- Class Weighting
- All of these
Answer: All of these
Imbalance handling: oversampling (SMOTE) increases minority, undersampling reduces majority, class weighting adjusts loss function. Choice depends on data size and model type.