Hyperparameter Tuning · seb1n/awesome-ai-agent-skills
Tune machine learning hyperparameters
Runs grid search, random search, Bayesian optimization, or Hyperband to find strong hyperparameter settings for a machine learning model within a compute budget, then reports which parameters matter most.
Good for
- Search learning rate and regularization ranges
- Compare grid, random, and Bayesian search
- Set up cross-validation and early stopping
- Source repository
- seb1n/awesome-ai-agent-skills
- Category
- Coding
Open-source skills are maintained by their authors and listed as published, with attribution. Results depend on how well the skill fits your task and material.
A good place to start
Tune the hyperparameters of my XGBoost model using Bayesian optimization within a limited compute budget.
Make your next great thing.
Bring a question, a file, or an idea that’s not quite there yet.