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Related Questions
- How can data augmentation techniques such as rotation, flipping, and color jittering minimize the impact of concept drift on machine learning models?
- In what ways can synthetic data generation through data augmentation help maintain the performance of a machine learning model over time as the underlying data distribution changes?
- What are some common challenges associated with using data augmentation to mitigate concept drift in machine learning models, and how can they be addressed?
- Can you explain the concept of 'data drift' and how data augmentation can help mitigate its effects on machine learning models?
- How does data augmentation impact the complexity of machine learning models and their susceptibility to concept drift?
- What are some strategies for deciding when to use data augmentation versus retraining a machine learning model from scratch when dealing with concept drift?
- How can data augmentation be used to adapt a pre-trained machine learning model to changes in the underlying data distribution over time?
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