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Related Questions
- What are some techniques for ensuring a diverse and representative negative example set?
- How can you prevent overfitting when collecting negative examples, and what are the consequences of underfitting?
- What strategies can be employed to avoid overfitting and underfitting in negative example collection, and how do they impact model performance?
- Can you explain the importance of data quality and quantity in negative example collection, and how it relates to overfitting and underfitting?
- What are some common pitfalls to avoid when collecting negative examples, and how can you mitigate their impact on model performance?
- How can you balance the trade-off between collecting too few or too many negative examples, and what are the implications for model overfitting and underfitting?
- What are some best practices for evaluating the effectiveness of negative example collection, and how can you identify potential issues with overfitting and underfitting?
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