How does FeatureUnion differ from ColumnTransformer?
PICTURE THIS: AN LLM TURN
Simple meaning
FeatureUnion runs transformers on the same data and concatenates their outputs.
WHY — sklearn Pipeline instead of guessing?
Why interviewers care about sklearn Pipeline:
question about sklearn Pipeline.
trade-offs, and what you would actually do on a AI / ML project - not buzzwords.
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:
- 1FeatureUnion runs transformers on
the same data and concatenates their outputs.
- 2ColumnTransformer routes named columns
to different transformers.
- 3Use ColumnTransformer for mixed
types and FeatureUnion when you want several views of the same matrix.
- 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
FeatureUnion runs transformers on the same data and concatenates their outputs. ColumnTransformer routes named columns to different transformers.