Should you add a missingness indicator when you impute?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
If missingness itself predicts the target, mean imputation hides that signal.
WHY — Feature Engineering instead of guessing?
Why interviewers care about Feature Engineering:
people who only read docs from people who shipped.
and tied to AI / ML work.
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:
- 1If missingness itself predicts
the target, mean imputation hides that signal.
- 2A binary missing flag
plus an impute value keeps both the filled number and the fact it was absent.
- 3Fit the imputer inside
the Pipeline so test rows do not set the fill value.
- 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
If missingness itself predicts the target, mean imputation hides that signal. A binary missing flag plus an impute value keeps both the filled number and the fact it was absent.