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Related Questions
- What are some common techniques for data augmentation in machine learning pipelines and how can they be used to improve model fairness and generalizability?
- How can data augmentation be used to address class imbalance and improve model performance on underrepresented groups?
- What are some strategies for introducing bias into data augmentation to better represent underrepresented groups?
- Can you provide examples of data augmentation techniques that can be used to improve model fairness and generalizability in image classification tasks?
- How can data augmentation be used to increase the diversity of training data and reduce overfitting in machine learning models?
- What are some best practices for evaluating the fairness and generalizability of machine learning models that have been trained with augmented data?
- Can you discuss the trade-offs between data augmentation and overfitting, and how to balance these competing factors in machine learning pipelines?
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