Agent skill · research science · aj-geddes
Model Hyperparameter Tuning
Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt
Why this skill is useful
Provides specific implementations for hyperparameter tuning methods, including grid search and Bayesian optimization, that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, optuna, pandas, scikit-learn, torch installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Model Hyperparameter Tuning Overview Hyperparameter tuning is the process of systematically searching for the best combination of model configuration parameters to maximize performance on validation data. When to Use When optimizing model performance beyond baseline configurations When comparing different parameter combinations systematically When fine-tuning complex models with many hyperparameters When seeking the best trade-off between bias, variance, and training time When improving model generalization on validation and test data When exploring parameter spaces for neural networks, tree models, or ensemble methods Tuning Methods Grid Search: Exhaustive search over parameter grid Random Search: Random sampling from parameter space Bayesian Optimization: Probabilistic model-based search Hyperband: Multi-fidelity optimization Evolutionary Algorithms: Genetic algorithm based search Population-based Training: Distributed parameter optimization Hyperparameters by Model Type Tree Models: maxdepth, minsamplessplit, learningrate Neural Networks: learningrate, batchsize, numlayers, dropout SVM: C, kernel, gamma Ensemble: nestimators, maxfeatures, minsamplesleaf Python Implementation Tuning Strategy by Model Tree Models: Focus on depth, minsamples, maxfeatures Boosting: Learningrate, nestimators, subsample Neural Networks: Learning rate, batch size, regularization SVM: C and kernel type are most important Best Practices Scale search space logarithmically for continuous parameters Use cross-validation for robust estimates Start with random search for initial exploration Use Bayesian optimization for final refinement Monitor for diminishing returns Deliverables Optimal hyperparameters found Performance metrics for top configurations Tuning efficiency analysis Visualization of parameter impact Tuning report and recommendations
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/model-hyperparameter-tuning