Observability & Evaluation · Software component
Rule Quality Scorer
Software componentObservability & EvaluationObservability & Evaluationarc:RuleQualityScorer
An evaluation component that measures a candidate rule's per-class coverage and precision on held-out validation data and combines them into a rule confidence score.
Responsibility. Scores the empirical reliability of candidate rules.
Relationships
reads dependency
writes dependency
is orchestrated by control
Design guidance
- SHOULD compute coverage separately for each decision class, since a trivial rule can reach 100% coverage of one class.
- MUST evaluate candidates on validation data separate from the data used to generate them.
Quantitative guidance
As stated by the sources; verify before use.
- Example thresholds: minimum 70% coverage and 80% precision to advance to expert review (Ch5.11).
- Fraud rule validated on six months of history: 145 confirmed frauds, 23 false positives, 86% precision, 72% coverage, confidence 0.82 (Ch5.11).
Classification
- Patterns
- Per-class coveragePrecisionRule confidence scoring
- Risks mitigated
- Trivial high-coverage rulesOver-broad rule conditions
Sources
- Ch5.11: T. Nguyen, "Rule-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.11. ISBN: 9798244538229.