Data leakage is a machine learning failure mode in which information from outside the training scope — test set data, future observations, or target-correlated features — enters the training process, producing artificially optimistic performance estimates. It arises from improper preprocessing ordering, insufficient temporal separation, or target variable leakage. The persistence mechanism is methodological documentation and validation protocols in machine learning practice. [formal: fuga | substrate: behavior | horizon: a moment | explicit: yes | epoch: 0.01]
Accepted ontology entry
leakage
Data leakage is a machine learning failure mode in which information from outside the training scope — test set data, future observations, or target-correlated features — enters the training process, producing artificially optimistic perfo…
Definition
Why it is in scope
Data leakage is a machine learning failure mode where information from outside the training scope — such as test set data, future information, or target variable correlates — inadvertently enters the training process. It produces overoptimistic performance estimates and is prevented through careful data partitioning, proper preprocessing order, and temporal validation protocols. It persists through methodological documentation, validation frameworks, and teaching practices in data science.
Names and aliases
- leakageen · CANONICAL
Relations from this entry
- cmruf04x1013cr671g8zou4lnINSTANCE_OF →
Leakage (data leakage in statistical/ML practice) is a specific kind of error — specifically, the error of inadvertently including information in training that would not be available at prediction time. A competent speaker in the domain calls leakage 'a type of modeling error.' Nearest kind is error.
Relations to this entry
- cmsetyrzs05xj3vv3fp6jbzjo← INSTANCE_OF
Data leakage IS a specific kind of leakage (information escaping unintended channels). In ML, it specifically describes information flow from training to evaluation data — a subtype of the broader leakage construct.
Record identity
- Created
- Aug 4, 2026, 2:41 PM UTC
- Content hash
- 3f03d3285575029e65de01b89c57d1adb189f8fa8e26e8a8232af2746dfd784c