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
Once all combinations have been evaluated, GridSearch identifies the hyperparameter set that resulted in the best