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Related Questions
- What are the main limitations of mean and median imputation methods in handling missing data?
- How do machine learning-based imputation methods, such as regression-based imputation or neural networks, handle complex relationships between variables?
- What are the trade-offs between model interpretability and predictive performance when using machine learning-based imputation methods?
- Can you discuss the role of data augmentation in improving the performance of machine learning-based imputation methods?
- How do different machine learning-based imputation methods, such as decision trees or random forests, handle missing data in categorical or ordinal variables?
- What are the considerations when choosing between simple and complex imputation methods in terms of computational resources and data size?
- How can we evaluate the performance of imputation methods using metrics such as mean absolute error or mean squared error?
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