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 AI Governance: Applied Mastery.
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 7 topics of every program are free with a free account.
- How AI Actually Works: Algorithms, Data, and Training · AI Fundamentals, AI Literacy & Professional Conduct
- AI for Innovation: Predictive Analytics Applications · Advanced AI Literacy, AI Literacy & Professional Conduct
- Train a model with your own hands and watch what it actually learns · Build Before You Govern, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.