AI Foundations
Overfitting
When a model learns the training data too closely and performs poorly on new data.
Example
A hiring model memorizes its training résumés but performs poorly on new applicants.
Why it matters
Recognizing it helps teams avoid mistaking memorized examples for useful ability.
You'll hear it
“The practice score keeps improving, but the independent checks are getting worse.”
Can you control it?
Not directly
No direct control. This describes a wider issue, concept or result rather than something you can simply switch on or off in a tool.
Common questions
- Can a very complicated system be more vulnerable?
- It can. Extra flexibility can let it learn accidental details as well as useful patterns.
- Can it happen with a small collection of examples?
- Yes. A limited collection gives the system fewer opportunities to distinguish lasting patterns from chance details.
- Can more training cause overfitting?
- Yes. Further practice on the same material can strengthen memorization without improving results on unfamiliar cases.