AI engineering glossary · ML fundamentals
What is data leakage?
Data leakage (also called Information leakage): A mistake where information that would not be available at prediction time gets into training, so the model looks excellent in testing but fails in real use. Common causes are features that reveal the answer, preprocessing on the full dataset, and duplicates across splits.
Data leakage: an example
A churn model uses 'account closed date' as a feature; it scores 99% in testing but the field is empty for customers who are still active.
Related terms
More ML fundamentals terms
- Data snooping
- Data-centric AI
- Dataset
- Decision threshold
- Discriminative model
- Distribution shift
- Double descent
- Early stopping
Where you learn it
Data leakage is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.