optuna

Hyperparameter optimization (HPO) for ML models using Optuna. Use when tuning learning rate, regularization, architecture choices, or any numeric/categorical hyperparameter. Covers create_study/optimize quickstart, sampler selection (TPE, CMA-ES, grid, random, NSGA-II), pruners for early stopping (MedianPruner, HyperbandPruner), distributed search with RDBS…

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