Hyperparameter optimization is the process of finding the best set of hyperparameters for a machine learning model to improve its performance. Hyperparameters are the settings or configurations that dictate how a model learns from data, and tuning them correctly can significantly enhance model accuracy and efficiency. This concept is closely related to methods like meta-learning, which seeks to improve learning processes, and neural architecture search, which automates the design of neural networks.
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