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Related Questions
- What is the optimal balance between reducing uncertainty and increasing the number of samples in active learning for improving model generalizability?
- How does the trade-off between exploration and exploitation affect the model's ability to generalize to new data?
- What are the key factors that influence the decision to prioritize reducing uncertainty versus increasing the number of samples in active learning?
- Can you explain how the choice of exploration strategy (e.g., uncertainty sampling, expected model change) impacts the model's generalizability?
- How does the model's capacity and complexity affect the trade-off between reducing uncertainty and increasing the number of samples?
- What are the implications of overfitting and underfitting on the model's generalizability in the context of active learning?
- Can you discuss the role of batch size and learning rate in the trade-off between reducing uncertainty and increasing the number of samples?
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