Model Training & Adaptation
Random search
Trying randomly selected AI setup choices to find ones that work well.
Example
A training process samples combinations of learning rates and regularization strengths.
Why people use it
It can explore useful training settings without testing every possible combination.
What you'll hear
“Try a range of choices, then compare them.”
What this means for you
Choose sensible sampling ranges and a reliable independent checking procedure.
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
- Is random search the same as random guessing without checking?
- No. Each sampled setup is trained and assessed systematically.
- Can two runs test different settings?
- Yes. Random selection can produce different trials unless the setup is controlled.
- Does it need sensible limits?
- Yes. Poorly chosen ranges can waste trials on settings unlikely to work.