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Related Questions
- Can active learning with uncertainty-based sampling reduce the number of labeled samples needed to achieve a certain level of model performance?
- How does uncertainty-based sampling compare to other active learning strategies, such as query-by-committee or support vector machines?
- Can uncertainty-based sampling be used in conjunction with other techniques, such as data augmentation or transfer learning, to further improve model generalization?
- How does the choice of uncertainty measure, such as entropy or variance, affect the performance of uncertainty-based sampling?
- Can uncertainty-based sampling be applied to other machine learning tasks, such as regression or reinforcement learning?
- How does uncertainty-based sampling perform on datasets with high-dimensional input spaces or large class imbalances?
- Can uncertainty-based sampling be used to identify and address model overfitting or underfitting?
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