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Discuss the trade-offs between using mean imputation and multiple imputation for handling missing values in a dataset. Explain how these methods differ in terms of their assumptions and the impact on model performance.
A
Mean imputation assumes missingness is at random, while multiple imputation accounts for uncertainty in imputation.
B
Mean imputation is always superior to multiple imputation in terms of preserving the original distribution.
C
Multiple imputation should not be used for numerical features.
D
Mean imputation is only suitable for categorical features.
