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Related Questions
- What are the benefits and drawbacks of using a large number of samples from the training data compared to focusing on the most informative ones?
- How does the choice of sample size impact the model's generalizability and accuracy?
- What are the potential trade-offs between including rare but informative samples versus prioritizing common but noisy ones?
- Can you explain the concept of 'information density' and how it relates to sample selection in machine learning?
- How can one balance the need for representative diversity in the training data with the desire for focused, high-quality samples?
- What are the implications of selecting samples based on specific criteria, such as data quality or feature relevance, versus random sampling?
- Can you discuss the relationship between sample size, overfitting, and model performance, and how to navigate these trade-offs?
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