Random Search
Definition
Random search compares randomly drawn settings within defined ranges.
An optimization method that randomly draws and evaluates configurations from defined value ranges.
Simply explained
Instead of trying every combination, possible settings are randomly selected and evaluated.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
Learning rate, batch size, and regularization are tested over 50 random trials.
Limits and pitfalls
- Choosing unsuitable distributions, using test data for selection, or ignoring computational budget.
Origin & history
Random search was established as a simple alternative to grid and model-based hyperparameter optimization.
Use in mid-sized companies
- Possible application: Learning rate, batch size, and regularization are tested over 50 random trials.
Frequently asked questions
What does random search mean?
An optimization method that randomly draws and evaluates configurations from defined value ranges. What mistakes should be avoided? Choosing unsuitable distributions, using test data for selection, or ignoring computational budget.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: