GridSearch

GridSearch is a technique used in machine learning to systematically search for the best combination of hyperparameters for a given model. Hyperparameters are settings that are not learned from the data during training, but rather are set before the training process begins. Examples include the learning rate of a neural network, the number of trees in a random forest, or the regularization strength in support vector machines.

The GridSearch algorithm works by defining a grid of possible values for each hyperparameter. It then iterates through all possible combinations of these values, training and evaluating the model for each combination. The performance of the model is typically measured using a cross-validation strategy to ensure that the evaluation is robust and not dependent on a single train-test split.

Once all combinations have been evaluated, GridSearch identifies the hyperparameter set that resulted in the best performance. This set is then used to train the final model. While GridSearch is exhaustive and guaranteed to find the optimal hyperparameters within the defined grid, it can be computationally expensive, especially when dealing with a large number of hyperparameters or a wide range of possible values. This is because the number of combinations grows exponentially with the number of hyperparameters. Alternative methods like RandomizedSearchCV or Bayesian optimization exist to address this computational challenge by exploring the hyperparameter space more efficiently.