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Answer: Mean imputation can introduce bias if the missingness is related to the target variable.
Using mean imputation for a numerical feature where the missing values are not missing at random can introduce bias into the model. This is because the mean imputation assumes that the missing values are randomly distributed, which is not the case if the missingness is related to the target variable. An alternative approach to handle such missing data could be using a more sophisticated imputation method, such as multiple imputation or using a machine learning model to predict the missing values based on other features.
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Discuss the implications of using mean imputation for a numerical feature in a dataset where the missing values are not missing at random. Explain how this could bias the model and suggest an alternative approach to handle such missing data.
A
Mean imputation can introduce bias if the missingness is related to the target variable.
B
Mean imputation is always unbiased and appropriate for all types of missing data.
C
Mean imputation should not be used for numerical features.
D
Mean imputation is only suitable for categorical features.