Hyper Parameter

Hyperparameters are the settings of a machine learning model that are not learned from data but are set beforehand, significantly impacting model performance and resource consumption. Current research focuses on optimizing hyperparameter selection across various model architectures, including deep neural networks, large language models, and Gaussian processes, often employing techniques like Bayesian optimization, evolutionary algorithms, and novel mathematical frameworks to improve efficiency and generalization. Effective hyperparameter tuning is crucial for achieving optimal model performance, reducing computational costs (including energy consumption), and enhancing the reliability and reproducibility of machine learning results across diverse applications.