Overfitting
When a model fits its training data too closely, memorizing its specifics and noise rather than learning patterns that generalize, so it performs well on training examples and poorly on new ones. Overfitting is one common reason a strong reported score does not survive contact with the real world.
Defined in 2 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
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.
When a model fits its training data too closely, memorizing its specifics and noise rather than learning patterns that generalize, so it performs well on training examples and poorly on new ones. Overfitting is one common reason a strong reported score does not survive contact with the real world.
A model that has learned the noise in its training data rather than the underlying signal, so it performs well on the past and poorly on the future; invisible in the rear-view mirror and a leading cause of forecast collapse.
When a model memorizes training data instead of learning generalizable patterns, performing well on test data but poorly on new real-world data.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- How AI Actually Works: Algorithms, Data, and Training · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- AI for Innovation: Predictive Analytics Applications · Advanced AI Literacy, Certified AI Practitioner: Workplace Foundations
- Train a model with your own hands and watch what it actually learns · Build Before You Govern, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Overfitting starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Decides where AI should be used, where it should not, and which use cases create measurable value, tied to the organization's priorities.
One lesson on this page teach it as a primary outcome.
Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.
One lesson on this page teach it as a primary outcome.
Knows when to trust, verify, escalate or override an AI recommendation, and stays accountable for the decision.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief AI Officerneeds it at depth
- Chief Information Officer, technology leadership editionneeds it at depth
- Operations Manager, AI-readyneeds it at depth
- AI Literacy Leadneeds it at depth
- Chief Technology Officer, AI engineering editionneeds working knowledge
- AI Adoption and Enablement Leadneeds it at depth
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 4; DOL AI Literacy Framework; EU AI Act Article 14; NIST AI RMF GOVERN.
One interview question about it
Fifty AI ideas are on the table. How do you pick the three worth doing, and how do you say no to the rest?
Asked of candidates for ai strategy and opportunity triage. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- A business, MBA or technology management degree carries it as a claim to back up: Strategy and opportunity triage are the core of the degree.
- Recent graduate in law, policy, business or data carries it as a claim to back up: Most graduates already use the tools daily.
- Teachers, trainers and instructional designers carries it as a claim to back up: Most educators are already using AI in preparation.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.