What signs of target leakage should you look for in EDA?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
Leakage shows up as features that would not be known at prediction time, perfect separation, or timestamps after the label.
WHY — EDA instead of guessing?
Why interviewers care about EDA:
who only read docs from people who shipped.
and tied to Data Science 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:
- 1Leakage shows up as
features that would not be known at prediction time, perfect separation, or timestamps after the label.
- 2Examples include using tomorrow's
churn flag, post-purchase fields to predict conversion, or an ID that encodes the label.
- 3Comparing metric distributions by
train versus future time splits helps catch it.
- 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
Leakage shows up as features that would not be known at prediction time, perfect separation, or timestamps after the label. Examples include using tomorrow's churn flag, post-purchase fields to predict conversion, or an ID that encodes the label.