A scientific approach to improving model performance | Machine Learning | Google for Developers
developers.google.com uses cookies to deliver and enhance the quality of its services and to analyze traffic. If you agree, cookies are also used to serve advertising and to personalize the content and advertisements that you see. Learn more This guide presents a structured approach to hyperparameter tuning in machine learning, emphasizing the categorization of hyperparameters and deliberate experiment design. The tuning process involves identifying scientific, nuisance, and fixed hyperparameters to efficiently explore the search space and optimize model performance. Researchers should analyze training curves, isolate the impact of key hyperparameters, and carefully evaluate candidate changes for consistent improvements. Automation is recommended for generating insightful plots and managing experiment results, while Bayesian optimization tools can be leveraged for final tuning. Addressing issues like overfitting and step-to-step variance is crucial for achieving reliable and reproducib