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Related Questions
- How can domain experts contribute to the creation of accurate negative examples in machine learning models?
- What are the key characteristics of high-quality negative examples, and how can domain knowledge inform their selection?
- Can you describe the potential consequences of using inaccurate or irrelevant negative examples in machine learning models?
- How can domain knowledge help identify biases in the collection process that may affect the quality of negative examples?
- What are some strategies for incorporating domain knowledge into the process of collecting and annotating negative examples?
- Can domain knowledge be used to develop more nuanced and context-specific negative examples that better reflect real-world scenarios?
- How can domain experts collaborate with machine learning engineers to ensure that negative examples are relevant and accurate?
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