Can I use this with specific Python libraries?

Yes, it is designed to generate code using popular machine learning frameworks such as scikit-learn and Optuna, making it compatible with most Python-based ML workflows.

How does the skill ensure the model doesn't overfit?

The skill follows best practices by implementing cross-validation within the generated search code to ensure that the chosen hyperparameters generalize well to unseen data.

Does it provide performance metrics after tuning?

Absolutely. After executing the search, Claude reports the best hyperparameter combination found along with corresponding metrics like accuracy, precision, recall, or RMSE.

What search strategies does this skill support?

The skill supports three primary optimization strategies: grid search for exhaustive exploration, random search for efficiency, and Bayesian optimization (via Optuna) for complex hyperparameter spaces.

ML Hyperparameter Tuner

Name: ML Hyperparameter Tuner
Author: intent-solutions-io

byintent-solutions-io

데이터 과학 및 ML

Optimizes machine learning model performance through automated grid search, random search, and Bayesian optimization.

소개

The Hyperparameter Tuner skill empowers Claude to significantly improve machine learning models by identifying optimal configurations. By analyzing model requirements and datasets, it generates executable Python code leveraging industry-standard libraries like scikit-learn and Optuna. It supports multiple search strategies—including grid search for exhaustive testing and Bayesian optimization for efficiency—while handling data validation, cross-validation, and performance reporting to ensure robust and accurate results.

주요 기능

Automated grid, random, and Bayesian optimization strategies
Integrated cross-validation to prevent model overfitting
Intelligent search space definition based on model architecture
0 GitHub stars
Executable Python code generation for scikit-learn and Optuna
Comprehensive performance reporting with detailed metric analysis

사용 사례

Reducing RMSE in regression tasks via Bayesian optimization with Optuna
Maximizing accuracy for classification models using automated grid search
Benchmarking different hyperparameter configurations to improve model generalization

소개

주요 기능

Automated grid, random, and Bayesian optimization strategies
Integrated cross-validation to prevent model overfitting
Intelligent search space definition based on model architecture
0 GitHub stars
Executable Python code generation for scikit-learn and Optuna
Comprehensive performance reporting with detailed metric analysis

사용 사례

Reducing RMSE in regression tasks via Bayesian optimization with Optuna
Maximizing accuracy for classification models using automated grid search
Benchmarking different hyperparameter configurations to improve model generalization