Can I use it with custom datasets?

Yes, the skill can generate code to load and preprocess your specific datasets before performing the tuning process.

Does it include cross-validation?

Yes, it follows best practices by implementing cross-validation to ensure model robustness and prevent overfitting during the search process.

What search strategies does this skill support?

It supports Grid Search, Random Search, and Bayesian Optimization (using Optuna) to find the best model parameters based on your specific requirements.

Which ML libraries is it compatible with?

It primarily integrates with industry-standard Python libraries like scikit-learn and Optuna for machine learning tasks.

How does Claude provide the tuning results?

Claude reports the best hyperparameter configuration found, provides the resulting performance metrics, and offers insights for further optimization.

Hyperparameter Tuner & Optimizer

Name: Hyperparameter Tuner & Optimizer
Author: jeremylongshore

byjeremylongshore

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884

Data Science & ML

Optimizes machine learning model performance by automatically searching for the most effective hyperparameter configurations.

About

This skill enables Claude to autonomously fine-tune machine learning models by identifying the optimal settings for accuracy and performance. By leveraging advanced search strategies such as Grid Search, Random Search, and Bayesian Optimization via Optuna, it streamlines the model development lifecycle. It handles everything from data validation and search-space definition to code generation and performance reporting, making it an essential tool for data scientists looking to improve model metrics like RMSE or accuracy without manual trial-and-error.

Key Features

884 GitHub stars
Detailed performance reporting and metric analysis
Seamless code generation for scikit-learn and other ML libraries
Automated Grid and Random Search implementation
Advanced Bayesian Optimization using Optuna integration
Cross-validation for robust performance evaluation

Use Cases

Minimizing error metrics in regression tasks using Bayesian optimization
Comparing different hyperparameter search strategies for a specific dataset
Optimizing Random Forest or Gradient Boosting model accuracy

About

Key Features

884 GitHub stars
Detailed performance reporting and metric analysis
Seamless code generation for scikit-learn and other ML libraries
Automated Grid and Random Search implementation
Advanced Bayesian Optimization using Optuna integration
Cross-validation for robust performance evaluation

Use Cases

Minimizing error metrics in regression tasks using Bayesian optimization
Comparing different hyperparameter search strategies for a specific dataset
Optimizing Random Forest or Gradient Boosting model accuracy