Adding indicator variables for missing values after imputation is not necessary, as the missing values have already been handled.

Adding indicator variables for missing values after imputation is important to capture the uncertainty or difference in the distribution of the missing values compared to the observed values.

Adding indicator variables is only useful for tree-based models, as they can directly split on these binary features.

Adding indicator variables is only useful for linear models, as they cannot handle missing values directly.