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Related Questions
- How does data augmentation impact the accuracy of models trained on imbalanced datasets?
- Can data augmentation techniques such as rotation and flipping be used to increase the number of samples in underrepresented classes?
- What are the potential risks of over-augmenting data and how can it affect model performance?
- Can data augmentation be used to create synthetic data that mimics the characteristics of underrepresented groups?
- What are the implications of using data augmentation on model interpretability and explainability?
- Can data augmentation be used in conjunction with other techniques such as oversampling and undersampling to address class imbalance issues?
- How does the choice of data augmentation technique impact the performance of models on underrepresented groups?
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