Grid Search

Grid Search is a Hyper-Parameter Tuning technique used to fine-tune the Hyper-Parameter of a model by exhaustively searching through a specified subset of hyperparameter combinations. This process involves training and evaluating the model for each combination to identify the set of hyperparameters that yields the best performance.


  1. Define Hyperparameter Grid: Specify the hyperparameters and their respective values to be explored in the grid search.
  2. Model Training and Evaluation: Train the model for each hyperparameter combination using cross-validation and evaluate its performance based on a predefined metric, such as accuracy, precision, recall, or F1 score.
  3. Identify Optimal Hyperparameters: Select the hyperparameter combination that yields the best performance on the validation set.

Hyperparameter Grid is a predefined set of hyperparameters and their corresponding values to be explored during the grid search.


  • Grid search is a computationally intensive method that systematically explores all possible hyperparameter combinations within the specified grid.
  • It is commonly used in combination with cross-validation to ensure that the identified optimal hyperparameters generalize well to unseen data.
  • Grid search helps in automating the process of hyperparameter tuning and finding the best configuration for a model, leading to improved performance and generalization.
  • While grid search exhaustively searches through the specified hyperparameter combinations, more advanced techniques such as randomized search and Bayesian optimization offer alternatives that can be more efficient in certain scenarios.