Tuning and Optimization

Tuning and Optimization

Complex models depend on many tunable parameters that must be optimized to maximize performance and accuracy. Rather than manually experiment with various combinations of values, automated hyperparameter optimization (HPO) tools systematically work to identify the optimal hyperparameters. HPO can use simple inefficient methods such as random or grid-based search of the hyperparameter space to look for optimum parameter values, or rely on more sophisticated techniques—like Bayesian optimization—to optimize parameters more efficiently. Many End-to-End platforms include some kind of HPO capability, often based on one of the many open source hyperparameter tuning packages.

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