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.


Process:

  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.
Note

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


Notes:

  • 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.