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Related Questions
- What are the key differences between in-distribution and out-of-distribution data, and how does data curation address these differences?
- Can you explain the concept of data drift and how it affects model performance on out-of-distribution data?
- How does data curation impact the robustness of machine learning models to concept drift and other forms of distribution shift?
- What role does data quality play in ensuring model performance on out-of-distribution data, and how can data curation improve data quality?
- Can you discuss the trade-offs between data curation and model complexity in terms of out-of-distribution performance?
- How does data curation impact the generalizability of machine learning models to new, unseen data, and what are the implications for out-of-distribution performance?
- What are some common pitfalls in data curation that can lead to poor out-of-distribution performance, and how can these pitfalls be avoided?
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