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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…
optuna
Use this skill for Optuna hyperparameter optimization workflows: studies, trials, samplers, pruners, storage, CLI, visualization, artifacts, integrations, and advanced APIs.
samplers-pruners
Choose, configure, customize, and debug Optuna search and pruning algorithms, including TPE, random, grid, CMA-ES, QMC, NSGA-II/III, GP, brute force, median, percentile, successive halving, Hyperband, threshold, patient, and Wilcoxon.
An independently imported catalog with original authors, licenses and source links. Classification is not a security audit.
Sources and import coverage