Glossary · Data & representations
Data Leakage
Unintended use of information during training or feature construction that would not be available at the real prediction point or belongs to a held-out evaluation boundary.
Why it matters
Leakage produces optimistic metrics that collapse when the system encounters genuinely unseen inputs.
In practice
Split data before fitting preprocessors, keep future information out of historical features, and isolate test labels and benchmark answers from prompts and tuning loops.
Common confusion
Leakage is not limited to duplicate rows. Global normalization statistics, timestamps, target-derived features, and repeated test-driven prompt edits can all leak information.
Related terms
Sources
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