Hyperparameter Importance

Hyperparameter optimization aims to identify the optimal settings for machine learning model parameters that control the learning process, significantly impacting model performance and efficiency. Current research focuses on efficiently determining hyperparameter importance across diverse model types, including neural networks (especially in climate modeling and quantum computing), and across multiple, potentially conflicting objectives (e.g., accuracy, fairness, and computational cost). Understanding hyperparameter influence is crucial for improving model training, reducing computational burden, and ensuring reliable and equitable predictions across various applications.