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Answer: Mean imputation assumes missingness is at random, while multiple imputation accounts for uncertainty in imputation.
Mean imputation assumes that the missing values are missing at random and replaces them with the mean of the available data. This method is simple but can introduce bias if the missingness is not random. Multiple imputation, on the other hand, creates multiple imputed datasets and aggregates the results, accounting for the uncertainty in the imputation process. This method is more robust but can be computationally intensive. The choice between these methods depends on the nature of the data, the assumptions about the missingness mechanism, and the computational resources available.
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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.
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