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Related Questions
- What are the key applications and scenarios where uncertainty sampling is most effective in active learning?
- How does uncertainty sampling with expected model change impact the overall efficiency and effectiveness of active learning?
- What are some potential drawbacks of relying solely on uncertainty sampling for active learning, and how can they be mitigated?
- Can you provide examples of how combining uncertainty sampling with expected model change can lead to improved model accuracy and reduced labeling costs?
- What are some potential limitations and challenges in implementing uncertainty sampling with expected model change in real-world active learning scenarios?
- How does the choice of uncertainty metric (e.g., entropy, margin, or variance) impact the effectiveness of uncertainty sampling with expected model change?
- What are some strategies for balancing the trade-off between exploration and exploitation in uncertainty sampling with expected model change?
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