Beginner · Research

Grid Search

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Exhaustively trying every combination of a predefined set of hyperparameter values.

Technical Definition

Exhaustively trying every combination of a predefined set of hyperparameter values.

How it works

Simple, deterministic, and embarrassingly parallel — but expensive: cost grows multiplicatively with the number of dimensions. Best for small, well-understood search spaces. Generally outperformed by random or Bayesian search for large spaces.

Related Concepts

• Hyperparameter Tuning — The process of finding optimal configuration values that control model training, such as learning rate, batch size, and architecture choices.
• Random Search — Sampling hyperparameter configurations at random instead of testing every combination.
• Bayesian Optimization — An adaptive hyperparameter search that uses a probabilistic model to choose the next configuration to try.