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Related Questions
- What are the common challenges associated with class-imbalanced datasets, and how does this impact the choice of evaluation metric?
- How do different evaluation metrics, such as accuracy and F1-score, affect the active learning strategy in class-imbalanced datasets?
- Why is it essential to consider the trade-off between precision and recall when selecting an evaluation metric for class-imbalanced datasets?
- How can the choice of evaluation metric influence the selection of instances for labeling in active learning, and what are the potential consequences?
- What role does the concept of cost-sensitive learning play in the choice of evaluation metric for class-imbalanced datasets?
- How can data augmentation and oversampling techniques impact the choice of evaluation metric and the active learning strategy in class-imbalanced datasets?
- What are the implications of using multiple evaluation metrics, such as accuracy, F1-score, and AUC-ROC, on the active learning strategy in class-imbalanced datasets?
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