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Related Questions
- How do you account for contextual understanding when selecting negative examples in machine learning?
- Can you explain the potential trade-offs between diversity and representative negative examples?
- How do you mitigate the risk of bias in negative examples when the context is complex or nuanced?
- What strategies can be used to ensure that negative examples are diverse and representative without introducing inaccuracies?
- How do you balance the need for negative examples to reflect real-world scenarios with the risk of bias or inaccuracies?
- Can you discuss the importance of contextual understanding in selecting negative examples and how it impacts model accuracy?
- What techniques can be used to evaluate the effectiveness of negative examples in reducing bias and improving model performance?
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