Area Under the ROC Curve
A metric that quantifies a classification model's ability to distinguish between classes across all decision thresholds, ranging from 0.5 (random performance) to 1.0 (perfect discrimination).
In plain language
A score from 0 to 1 that measures how well an AI can tell two things apart. A score of 1.0 is perfect, 0.5 is random guessing. It's like a report card for how accurately the AI distinguishes between categories like cats and dogs.
Why this matters
ROC-AUC is a standard metric for evaluating classification model performance and is often used in governance reviews to assess whether an AI system meets minimum performance thresholds before deployment.
Relevance
ImplementationTechnical teams use ROC-AUC to validate model performance against governance-defined requirements. It provides a standardised way to demonstrate to oversight bodies that performance meets acceptance criteria.
Related terms
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