A bike-share cancellation model includes a column named refund-processed-at. That timestamp is written only after a reservation is already canceled. Lab scores look perfect, then new reservations score poorly. What problem is this?
Select an answer to reveal the explanation.
Short Explanation
Think of peeking at the final exam answer key while you study. A refund timestamp that only appears after cancel is future information the model would not have at reservation time. That is leakage, not a missing GPU, and a perfect lab score is the opposite of underfitting.
Full Explanation
Data leakage is using a feature that would not be available when the model must predict. A refund timestamp written after cancel is that kind of future information, so the lab score is inflated and new reservations fail. A perfect lab score is the opposite of underfitting. Leakage is a data-design problem, not a missing GPU. A post-event timestamp is not a legitimate live input at reservation time.