Explain point-in-time correct feature joins. Why are they essential when building training data from historical events?
formulate your answer, then —
tldr
Point-in-time joins ensure training examples use only feature values available at the historical prediction time. They prevent temporal leakage from current tables, future aggregates, backfills, and late-arriving data. Data availability time matters as much as event time.
follow-up
- Why is event time alone insufficient for leakage prevention?
- How can backfills corrupt old training examples?
- What tests would you add to validate point-in-time correctness?