Why add a missing-indicator column when you impute?
PICTURE THIS: DJANGO MVT
Simple meaning
The fact of missingness can itself predict the outcome, for example users who skip billing fields.
WHY — Missing Data instead of guessing?
Why interviewers care about Missing Data:
on Missing Data.
the situation, the default choice, and one exception - that reads as experience.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1The fact of missingness
can itself predict the outcome, for example users who skip billing fields.
- 2An indicator lets the
model use that signal while the imputed value fills the numeric hole.
- 3Indicators help most when
missingness is informative, and they add noise when missingness is rare and MCAR.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
The fact of missingness can itself predict the outcome, for example users who skip billing fields. An indicator lets the model use that signal while the imputed value fills the numeric hole.