Hyperparameter Tuning

Hyperparameter tuning involves selecting the best set of hyperparameters for a machine learning model to achieve optimal performance. Unlike model parameters, which are learned during training, hyperparameters are set before the training process begins. Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization, which help in systematically exploring the hyperparameter space to find the most effective configuration.