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In the context of feature engineering, explain why it is crucial to add indicator variables for missing values that have been imputed or replaced. Discuss the implications of not including such indicators and how they can affect model performance.
A
Indicator variables help in identifying the original missingness, which can be a valuable feature for the model.
B
Indicator variables are not necessary as the model can infer the missingness from the data itself.
C
Indicator variables increase the dimensionality of the data, making the model more complex.
D
Indicator variables should only be used for categorical data, not for numerical data.
