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Related Questions
- Can uncertainty-based sampling be combined with data augmentation to create more diverse and representative training datasets?
- How does uncertainty-based sampling interact with transfer learning, and can it enhance the effectiveness of pre-trained models?
- Can uncertainty-based sampling be used in conjunction with other regularization techniques, such as dropout or L1/L2 regularization, to improve model generalization?
- How does uncertainty-based sampling impact the trade-off between model complexity and generalization performance?
- Can uncertainty-based sampling be applied to different types of machine learning models, such as neural networks or decision trees?
- How does uncertainty-based sampling affect the interpretability of model predictions, and can it provide insights into model uncertainty?
- Can uncertainty-based sampling be used to detect and mitigate the effects of dataset bias or adversarial attacks on model generalization?
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