Asked about The questions every ML interview asks, answered properly

Your dataset has 20% missing values in an important column. What do you do?

An answer that works

First find out why it is missing, since missingness can itself be informative. Options are imputation with a missing indicator, models that handle missing values natively such as gradient boosting, or dropping the column if it is missing for a reason that makes it unusable. Impute inside the cross-validation pipeline.

Then they push further

Why add an indicator column?

If values are missing for a reason related to the outcome, such as customers declining to state income, the fact of missingness predicts. Imputation alone erases that signal.

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