Model Training & Adaptation
Grid search
Comparing a planned set of combinations to find suitable AI settings.
Example
A team compares several learning-speed settings with several levels of complexity.
Why people use it
It systematically compares a planned set of possible training settings.
What you'll hear
“Try every combination on this list.”
What this means for you
Keep some examples aside for a final check after choosing settings.
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
- Can the tested settings include choices rather than numbers?
- Yes. A comparison can try different methods or options as well as numerical settings.
- Can the number of trials grow quickly?
- Yes. Adding more settings multiplies the number of combinations to try.
- Can it miss a good choice?
- Yes. A useful value between the listed choices will not be tested.