Point-in-Time Correctness: Avoiding Data Leakage in ML
A point-in-time correct join is a time-traveling lookup for ML features, grabbing the most recent values known *at the time of an event*. It's vital when building training data from feature tables that update at different rates to prevent data leakage.
A point-in-time correct join is a time-traveling lookup for ML features. For each labeled event, it finds the latest feature values that were known *at that exact moment*, not a second later. This is crucial when creating training data from feature tables that update at different cadences. The primary footgun it prevents is data leakage: accidentally using future information to train, creating an overly optimistic model that collapses in production because it learned to cheat.
Read the original → hopsworks.ai
- #mlops
- #feature engineering
- #data leakage
- #sql
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