Grid Search

Grid Search is a hyperparameter tuning technique used in machine learning and statistics to optimize a model's performance. It systematically explores a predefined set of hyperparameters, which are the configuration settings that influence the model's learning process.In a grid search, the user specifies a grid of hyperparameter values and the algorithm exhaustively tests every combination from this grid to find the set that results in the best performance metric, such as accuracy, precision, or recall, based on a validation dataset. This method helps in identifying the optimal hyperparameters that lead to better model generalization and effectiveness.Despite its effectiveness, grid search can be computationally expensive, particularly when the grid contains many combinations and when the model itself is complex. Variants of grid search include random search, which samples hyperparameter values randomly rather than exhaustively, and more advanced techniques like Bayesian optimization, which aim to improve efficiency in the search process.