automl-optimizer
Automated machine learning with hyperparameter optimization using Optuna, Hyperopt, or AutoML libraries. Activates for "automl", "hyperparameter tuning", "optimize hyperparameters", "auto tune model", "neural architecture search", "automated ml". Systematically explores model and hyperparameter spaces, tracks all experiments, and finds optimal configurations with minimal manual intervention.
npx skills add majiayu000/claude-skill-registry --skill automl-optimizer-anton-abyzov-specweave --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AutoML Optimizer ## Overview Automates the tedious process of hyperparameter tuning and model selection. Instead of manually trying different configurations, define a search space and let AutoML find the optimal configuration through intelligent exploration. ## Why AutoML? **Manual Tuning Problems**: - Time-consuming (hours/days of trial and error) - Subjective (depends on intuition) - Incomplete (can't try all combinations) - Not reproducible (hard to document search process) **AutoML Benefits**: - ✅ Systematic exploration of search space - ✅ Intelligent sampling (Bayesian optimization) - ✅ All experiments tracked automatically - ✅ Find optimal configuration faster - ✅ Reproducible (search process documented) ## AutoML Strategies ### Strategy 1: Hyperparameter Optimization (Optuna) ```python from specweave import OptunaOptimizer # Define search space def objective(trial): # Suggest hyperparameters params = { 'n_estimators': trial.suggest_int('n_estimators', 100, 1000), 'max_depth': trial.suggest_int('max_depth', 3, 10), 'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True), 'subsample': trial.suggest_float('subsample', 0.5, 1.0), 'colsample_bytree': trial.su
- Overview
- Why AutoML?
- AutoML Strategies
- Strategy 1: Hyperparameter Optimization (Optuna)
- Strategy 2: Algorithm Selection + Tuning
- Strategy 3: Neural Architecture Search (NAS)
- AutoML Frameworks Integration
- Optuna (Recommended)
- Auto-sklearn
- H2O AutoML
- Best Practices
- 1. Start with Default Baseline
- 2. Use Cross-Validation
- 3. Set Reasonable Search Budgets
Run AutoML optimization Compare algorithms Show optimization history
What does the automl-optimizer skill do?
Automated machine learning with hyperparameter optimization using Optuna, Hyperopt, or AutoML libraries. Activates for "automl", "hyperparameter tuning", "optimize hyperparameters", "auto tune model", "neural architecture search", "automated ml". Systematically explores model and hyperparameter spaces, tracks all experiments, and finds optimal configurations with minimal manual intervention.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill automl-optimizer-anton-abyzov-specweave --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.