Grid Search
Definition
Grid search systematically tests predefined combinations of hyperparameters.
For each value from a fixed parameter grid, combinations are tried and typically evaluated with a validation strategy. Effort grows quickly with number and scope of parameters.
Simply explained
Like trying every allowed setting in a table and evaluating each variant by the same rule.
Why it matters in practice
Relevant for small, manageable search spaces and reproducible model comparisons.
Example from business
A classifier is compared with multiple learning rates, regularization values, and model sizes via cross-validation.
Limits and pitfalls
- Too large grids cause high costs; do not use test data for selection; consider only the best value and ignore variance; do not call grid search automatically efficient.
Origin & history
Grid search is a classical hyperparameter optimization method. Scikit-learn offers a popular implementation with GridSearchCV; Bergstra and Bengio showed in 2012 that random search can be more efficient in many high-dimensional spaces.
Use in mid-sized companies
- Possible application: transparent selection of a few less important model parameters in a limited pilot project.
Frequently asked questions
Is grid search always sensible?
No, it quickly becomes expensive with many parameters. May the test set be used for selection? No, a separate validation should be used for that.
Sources
- GridSearchCV · scikit-learn, GridSearchCV
- Bergstra & Bengio (2012) · Bergstra & Bengio (2012)
Sources checked on 10.10.2026.
Editorially reviewed on: