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Related Questions
- What are the common methods used to balance data for underserved groups in machine learning models?
- How does data imbalance affect the performance of machine learning models on tasks involving underserved groups?
- What are some real-world examples of data imbalance in machine learning models and how was it addressed?
- What are the potential consequences of neglecting data balancing for underserved groups in machine learning models?
- Can oversampling or undersampling be used to balance data for underserved groups, and what are their limitations?
- How does data balancing impact the fairness and generalizability of machine learning models on underserved groups?
- What role does data balancing play in reducing bias and increasing representation of underserved groups in machine learning models?
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