Hyperparameter Optimization
HPOAlso called: HPO, Hyperparameter Tuning
The process of automatically or systematically searching for the combination of hyperparameter values that produces the best performance for an AI model or system according to one or more evaluation metrics.
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Related terms
The process of selecting and adjusting the hyperparameters of an AI model or system through controlled experiments to identify the configuration that delivers the best performance according to one or more evaluation metrics.
Grid SearchA hyperparameter optimization technique that systematically evaluates all predefined combinations of parameter values to identify the configuration that produces the best performance for an AI model or system.
Random SearchAn optimization technique that evaluates randomly selected hyperparameter configurations from a defined search space.
Bayesian OptimizationAn optimization method that uses probabilistic models to efficiently identify promising parameter configurations with minimal evaluation runs.
Experiment TrackingThe process of recording and managing information about AI experiments, including datasets, models, hyperparameters, code versions, metrics, and outcomes to enable reproducibility and comparison.