Back to Catalog
Data Science
Data Quality
Data Leakage Prevention
Runtime-enforced evaluate/assess boundary that rejects repeated test-set assessment.
Intent & Description
📋 Context
Data leakage affected 294 published papers across 17 scientific fields. The grammar decomposes the supervised learning lifecycle into kernel primitives with hard constraints that reject leakage classes at call time.
Real-world Use Case
When building supervised ML systems to ensure that evaluation metrics are not artificially inflated.
Source
Advantages
- Prevents selection leakage
- Prevents memorization leakage
- Runtime enforcement
Disadvantages
- Additional complexity
- Requires strict typing of data flows
Implementation Example
# Data Leakage Prevention
class EvidenceType: pass
# Runtime guard prevents test data in training