What metrics can I optimize for?

You can optimize for any standard evaluation metric including accuracy, precision, recall, F1-score, or root mean squared error (RMSE).

How do I trigger this skill in Claude?

Use natural language commands like 'tune hyperparameters', 'optimize model', 'perform grid search', or 'run Bayesian optimization' followed by your model details.

Which search strategies does this skill support?

It supports Grid Search for exhaustive testing, Random Search for efficiency, and Bayesian Optimization (via Optuna) for complex hyperparameter spaces.

Does it prevent overfitting during the tuning process?

Yes, the skill follows best practices by implementing cross-validation to ensure that the chosen hyperparameters are robust and generalize well.

Can it handle different machine learning frameworks?

Yes, it generates Python code compatible with popular libraries such as Scikit-learn and Optuna for a wide range of model types.

Machine Learning Hyperparameter Tuner

Name: Machine Learning Hyperparameter Tuner
Author: BbgnsurfTech

byBbgnsurfTech

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데이터 과학 및 ML

Optimizes machine learning model performance by automatically searching for the best hyperparameter configurations using grid, random, or Bayesian search strategies.

This skill empowers developers and data scientists to maximize the accuracy and efficiency of their machine learning models by automating the complex process of hyperparameter tuning. It intelligently analyzes user requirements to generate production-ready Python code utilizing industry-standard libraries like scikit-learn and Optuna. Whether you need an exhaustive grid search or a sophisticated Bayesian optimization, the skill handles data validation, cross-validation setup, and performance metric reporting to ensure robust and reliable model improvements without manual trial and error.

주요 기능

013 GitHub stars

02Automated Python code generation for Scikit-learn and Optuna

03Integrated cross-validation to prevent model overfitting

04Smart detection of optimal search spaces and model requirements

05Supports Grid, Random, and Bayesian optimization strategies

06Comprehensive performance metric reporting and analysis

사용 사례

01Fine-tuning a Random Forest classifier for improved accuracy on tabular data

02Optimizing Gradient Boosting regressors using Bayesian techniques to minimize RMSE

03Comparing different search strategies to find the most efficient path to model optimization

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bbgnsurftech/claude-skills-collection skill-adapter

For use in Claude.ai and ChatGPT

Download Skill