What is the point of using MissingIndicator in Scikit-learn? I have recently discovered Sklearn's MissingIndicator but still wondering how could it improve the usual machine learning work. Clear that

from sklearn.impute import MissingIndicator
indicator = MissingIndicator()
X_mask = indicator.fit_transform(X)


could result in  X_mask as boolean indicator array of missing values in original X. But how could it be utilized actually beyond that it keeps track where imputed values are? I know that flagging NAs in a separate column per features could be useful for learners but here X_mask  is not combined with the imputed X so this is seemingly not the point.

Similar question arises with putting MissingIndicator in the ML pipeline:

transformer = FeatureUnion(
     transformer_list=[
         ('features', SimpleImputer(strategy='mean')),
         ('indicators', MissingIndicator())])


What is the point to add the pure mask array to the imputed array here?
Good question. Here is my take on it:

The following piece code creates the union of both the imputed features and the indication of which ones were imputed.

transformer = FeatureUnion(
     transformer_list=[
         ('features', SimpleImputer(strategy='mean')),
         ('indicators', MissingIndicator())])


I can see it becoming useful for a learner that may learn to not rely as much on features that are often missing. Or perhaps, the fact that some features are missing at all can be helpful for the learner. As, missing values can mean that something happened differently in the data collection, which can be useful to make a prediction.

Let's say I have the following problem:

I want to classify users based on their watching habits on my fancy streaming platform. Users have to rate a movie thumbs up or thumbs down. Of course, not all my users will watch all movies, so I'll have blank values. By using let's say the SimpleImputer, I'll basically make an educated guess on how they would have liked the movies they didn't watch. That can be useful as then I can compare users across all movies. However, knowing which movies the user has watched altogether is probably as informative. This is what the MissingIndicator provides you.