The COMPEL Glossary Graph visualizes relationships between framework terminology, showing how concepts interconnect across domains, stages, and pillars. Term nodes cluster by pillar affiliation while cross-references reveal semantic dependencies — for example, how risk appetite connects to control effectiveness, model governance, and assurance requirements. This network representation helps practitioners navigate the framework vocabulary and understand that COMPEL terminology forms a coherent conceptual system rather than isolated definitions.
COMPEL Glossary / data-leakage
Data leakage
Information from the test or validation set inadvertently entering training — through preprocessing, feature engineering, target encoding, or time-ordered splits — inflating offline metrics and producing over-optimistic ship decisions.
What this means in practice
A leading cause of offline-to-online performance gaps; defense requires disciplined split protocols and temporal holdouts.
Synonyms
target leakage , feature leakage , evaluation leakage
See also
- Offline evaluation — Assessment of an AI system against static datasets — training hold-out, validation set, benchmark corpus — without exposure to live user traffic.
- Benchmark contamination — The presence of benchmark test data in foundation-model training corpora — whether through web crawling or deliberate inclusion — inflating reported benchmark scores and breaking the comparability of benchmark results across models.
- Reproducibility — The property that re-running an experiment with the same code, data, and configuration produces the same results within declared tolerance.