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Related Questions
- Can data augmentation techniques inadvertently introduce bias into the model, leading to less accurate or less interpretable results?
- How can the choice of data augmentation technique impact the model's ability to generalize to unseen data?
- What are some potential risks of over-reliance on data augmentation for improving model explainability, and how can they be mitigated?
- Can data augmentation techniques obscure the underlying relationships between input features and target variables, making it more difficult to interpret model outputs?
- How can the effects of data augmentation on model explainability be evaluated and measured?
- Can data augmentation techniques be used to improve model interpretability in cases where the underlying data is noisy or incomplete?
- What are some strategies for using data augmentation to improve model explainability while minimizing the risk of overfitting or underfitting?
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