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Related Questions
- How does active learning's iterative approach to model improvement impact the overall training process in terms of scalability?
- Can you compare the computational resources required for active learning versus traditional supervised learning?
- What are the key differences in the labeling process between active and traditional supervised learning?
- How does active learning's focus on high-confidence samples affect the model's generalizability to new, unseen data?
- Can you discuss the role of uncertainty-based sampling in active learning and its implications for efficiency?
- How does active learning's ability to adapt to changing data distributions impact its ability to improve model performance over time?
- What are the trade-offs between the initial model quality and the efficiency of active learning versus traditional supervised learning?
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