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Related Questions
- How do ensemble methods, such as stacking or bagging, contribute to reducing annotator variability in machine learning tasks?
- Can ensemble methods help to mitigate the impact of individual annotator biases on the overall judgment consistency?
- What are some common techniques used to combine annotator judgments in ensemble methods, and how do they affect consistency?
- In what scenarios might ensemble methods be particularly effective in improving annotator judgment consistency?
- How do ensemble methods handle cases where annotators have different levels of expertise or experience?
- Can ensemble methods be used to detect and correct annotator errors or inconsistencies?
- What are some potential limitations or challenges associated with using ensemble methods to improve annotator judgment consistency?
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