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Related Questions
- What are the key differences between data augmentation and imputation techniques like mean/mode/median imputation in terms of data distribution and model performance?
- How does data augmentation handle missing values differently than other popular imputation methods?
- Can data augmentation be used in conjunction with other imputation techniques, such as mean/mode/median imputation, to improve model performance?
- What are the potential downsides of using data augmentation for missing value imputation compared to other methods?
- How does data augmentation impact the interpretability of models when used for missing value imputation?
- Can data augmentation be used for imputing both numerical and categorical missing values?
- What are the computational costs associated with using data augmentation for missing value imputation compared to other methods?
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