Grid Search
Grid Search is a hyperparameter tuning technique in machine learning that systematically explores a predefined set of hyperparameter values to find the optimal configuration for a model. It works by training and evaluating the model on every possible combination of hyperparameters within the specified grid, typically using cross-validation to assess performance. This exhaustive approach ensures that the best hyperparameters are identified from the given search space, though it can be computationally expensive for large grids.
Developers should use Grid Search when they need a reliable and straightforward method to optimize model performance, especially for small to medium-sized hyperparameter spaces where computational cost is manageable. It is particularly useful in scenarios where hyperparameters have discrete values or a limited range, such as tuning the number of neighbors in k-NN or the depth of a decision tree, to prevent overfitting and improve accuracy in supervised learning tasks like classification or regression.