11.05 · Walkthrough
Point-in-Time Feature Retrieval
Generate a point-in-time correct training dataset from historical feature values and labeled events.
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Point-in-time feature retrieval builds training rows using only feature values that would already have existed when each labelled event occurred. It prevents leakage by joining entities and event times to historical feature records with availability-aware time constraints, so offline training reflects the same information the model would have seen at prediction time.
What this lesson answers
- how point in time feature retrieval prevents leakage
- how to join labels to historical features correctly
- event time versus feature availability time
Notes
Point-in-time feature retrieval is the process of building a training dataset where every feature value is taken from the past relative to the labeled event it is paired with. For each training example, you have an entity, such as a user, account, device, or merchant, and an event timestamp, such as the time a transaction occurred or the time a prediction would have been made. The retrieval logic must return the latest valid feature values that were available at that timestamp, not values computed later.
Common questions
- What is point-in-time feature retrieval?
- Point-in-time feature retrieval is the construction of training examples where each feature is selected as of the event time for that row. For a user, account, device, or similar entity, the retrieval picks the latest feature value that was valid and available before the prediction would have been made.
- Why does point-in-time correctness matter for model training?
- Without point-in-time correctness, training can include information from after the labelled event. That creates leakage: validation metrics look strong because the model has seen future-derived signals, but production performance drops when those signals are unavailable. Correct retrieval gives the model the same information boundary it will face when serving.
- What should I check when validating a point-in-time dataset?
- Check that feature records joined to each event do not come after the event timestamp or after the time they became available. Make missing historical values explicit, keep label generation separate from feature computation, and verify that offline feature definitions match the ones used for online serving.
Short definition: what is Point-in-Time Feature Retrieval?