Hyperparameter Tuning
The process of searching for the optimal hyperparameter values (e.g., via grid search, Bayesian optimization) to maximize model performance.
Definition
A systematic exploration—grid, random, or Bayesian search—over hyperparameter spaces to find configurations that deliver the best balance of accuracy, generalization, and resource use. Governance best practices include defining search ranges, limiting compute budgets, tracking all experiments in MLflow or similar, and freezing configurations once validated to avoid “drift” in production.
Real-World Example
An NLP team uses Bayesian optimization to tune a transformer’s learning rate, batch size, and dropout rate over 50 trials. They record each trial’s metric and hyperparameters in an experiment-tracking dashboard, then select the configuration that achieves highest F1 on a held-out test set, ensuring reproducible and optimized performance.
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