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Related Questions
- Can you explain the concept of active learning and how it can be applied to imbalanced datasets?
- How does active learning select the most informative samples for labeling, and what are the benefits of this approach?
- What are some common methods used in active learning for imbalanced datasets, such as uncertainty sampling or query-by-committee?
- How can active learning be used to reduce the impact of class imbalance on model performance, and what are the key considerations?
- Can you discuss the trade-offs between active learning and other methods for handling imbalanced datasets, such as oversampling or undersampling?
- How can active learning be integrated with other techniques, such as transfer learning or ensemble methods, to improve model performance on imbalanced datasets?
- What are some common challenges and limitations of using active learning for imbalanced datasets, and how can they be addressed?
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