Recall
Of all the cases that truly were positive, the share the model caught. High recall means few misses. Prioritized when a false negative is the more expensive mistake. Formula: true positives divided by all actual positives.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Transformation Professional (CATP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
Of all the cases that truly were positive, the share the model caught. High recall means few misses. Prioritized when a false negative is the more expensive mistake. Formula: true positives divided by all actual positives.
Of the cases that were actually positive, the proportion the model correctly flagged; a measure of how many real cases a system catches.
Of the cases that were actually positive, the fraction the model flagged. Rises when you loosen the model to flag more.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Fixing the model, breaking it again: why fixes are never free · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- How AI Actually Works Under the Hood: Architectures and Limits for Policy People · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
- Analytics, Telemetry and Observability · Technology, Platforms and Vendors, Certified AI Transformation Professional (CATP)
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.