tuning-hyperparameters
byandikarachman· 19 skills
Hyperparameter tuning workflow reference -- strategy selection, Bayesian optimization with Optuna, search space design, and result analysis. Use when /ds:experiment needs to choose a tuning strategy, design search spaces, or analyze tuning runs.
How to install tuning-hyperparameters?
andikarachman/data-science-plugin/tuning-hyperparameters
$npx -y skills add andikarachman/data-science-plugin --skill tuning-hyperparametersInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/andikarachman/data-science-plugin" --skill "andikarachman/data-science-plugin/tuning-hyperparameters"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/andikarachman/data-science-plugin" that are relevant to the current task. Run `npx skills add "https://github.com/andikarachman/data-science-plugin"` and select the relevant skills, then follow their instructions.
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