Model Training & Adaptation
Early stopping
Stopping AI training when checks show it is no longer improving.
Example
Training stops after independent checking performance fails to improve for several checks.
Why people use it
It avoids spending more training time after useful performance stops improving.
What you'll hear
“Stop when it stops getting better on fresh examples.”
What this means for you
Track a suitable independent checking measure rather than optimizing training fit alone.
Can you control it?
Developer-only
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does one worse check always stop training?
- No. Many setups allow a period without improvement before deciding to stop.
- Can stopping too early hurt performance?
- Yes. The system may not yet have learned the useful patterns it could learn.
- Why not judge only on the examples used for learning?
- It can keep improving on familiar examples while becoming less useful on new ones.