hyperparameter-optimization

Hyperparameter optimization is a critical aspect of machine learning, focusing on identifying the best set of hyperparameters for a given model. Unlike model parameters, which are learned during training, hyperparameters are set prior to training and can significantly impact the model's performance. Techniques such as grid search, random search, and Bayesian optimization are commonly used to explore the hyperparameter space effectively.