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Answer: Indicator variables help in identifying the original missingness, which can be a valuable feature for the model.
Including indicator variables for missing values that have been imputed or replaced is crucial because it allows the model to differentiate between the original missing data and the imputed values. This can provide additional information to the model about the data distribution and potential patterns in the missingness, which can improve model performance. Not including such indicators might lead the model to treat imputed values as real data, potentially biasing the model.
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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.