Grid Search

Grid search is a hyperparameter tuning technique used in machine learning to optimize model performance. It systematically explores a specified subset of hyperparameters by training the model on each combination of parameter values. The main characteristics of grid search include its exhaustive nature, which can lead to finding the best model configuration, and its simplicity, making it accessible for beginners. Common use cases involve optimizing algorithms like support vector machines, decision trees, and neural networks by evaluating their performance on validation datasets. However, grid search can be computationally expensive, particularly with large datasets or complex models.