What optimization methods are supported?

The skill supports Grid Search, Random Search, and Bayesian Optimization (typically utilizing the Optuna library) to find the best model parameters.

Can I use this with any machine learning library?

Yes, while it defaults to popular libraries like scikit-learn and Optuna, it can generate Python code for most standard ML frameworks.

How do I trigger this skill in Claude Code?

You can trigger it using natural language commands like 'tune hyperparameters', 'optimize model', or by specifying a method like 'run a grid search'.

Does it handle cross-validation?

Yes, the skill follows best practices by implementing cross-validation to ensure that the optimized parameters generalize well to unseen data.

ML Hyperparameter Tuner

Name: ML Hyperparameter Tuner
Author: jeremylongshore

byjeremylongshore

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883

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データサイエンスとML

Optimizes machine learning models using automated search strategies like grid, random, and Bayesian optimization to maximize performance.

This skill empowers Claude to automatically find the most effective hyperparameter configurations for machine learning models, significantly reducing the manual trial-and-error process. By leveraging techniques like grid search, random search, and advanced Bayesian optimization via Optuna, it explores hyperparameter spaces to improve metrics like accuracy, RMSE, and precision. It handles the entire lifecycle from data validation and search space definition to generating executable Python code and reporting the final optimized results, ensuring robust models through integrated cross-validation.

主な機能

01883 GitHub stars

02Provides detailed performance metrics and comparative analysis of results

03Automates data validation and error handling for robust model tuning

04Implements cross-validation to prevent overfitting and ensure result reliability

05Supports multiple search strategies including Grid, Random, and Bayesian optimization

06Generates production-ready Python code using scikit-learn and Optuna

ユースケース

01Optimizing Random Forest or Gradient Boosting models for classification and regression tasks

02Comparing the efficiency of different optimization strategies on a specific dataset

03Refining hyperparameters for complex models when manual tuning is computationally expensive

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills hyperparameter-tuner

For use in Claude.ai and ChatGPT

Download Skill