Which search strategies does this skill support?

It supports three primary strategies: Grid Search for exhaustive tuning, Random Search for efficiency, and Bayesian Optimization via Optuna for complex parameter spaces.

How does it ensure the model doesn't overfit?

The skill follows best practices by implementing cross-validation during the search process to ensure the performance metrics are robust and generalizable.

Can I use this with custom evaluation metrics?

Yes, you can specify various evaluation metrics such as accuracy, precision, recall, F1-score, or RMSE depending on your specific model requirements.

Does the skill handle the entire ML pipeline?

The skill generates comprehensive Python code that covers data loading, preprocessing, model training, hyperparameter searching, and final evaluation.

Hyperparameter Tuner

Name: Hyperparameter Tuner
Author: jeremylongshore

byjeremylongshore

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896

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数据科学与机器学习

Optimizes machine learning model performance by automatically identifying and implementing the most effective hyperparameter configurations.

The Hyperparameter Tuner skill empowers Claude to systematically improve machine learning model accuracy and efficiency through automated search strategies. It supports grid search, random search, and advanced Bayesian optimization via Optuna, handling everything from search space definition to code generation and performance reporting. This skill is essential for data scientists looking to move beyond default parameters and squeeze every bit of performance out of their models while ensuring robustness through built-in cross-validation techniques.

主要功能

01Automated grid, random, and Bayesian search strategies

02Automated Python code generation for training and evaluation

03Built-in cross-validation to ensure model robustness

04Seamless integration with scikit-learn and Optuna

05Comprehensive performance reporting and metric comparison

06896 GitHub stars

使用场景

01Optimizing Random Forest or Gradient Boosting parameters for improved classification accuracy

02Minimizing error rates in regression models using Optuna-driven Bayesian optimization

03Automating the comparison of different hyperparameter ranges during model development

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills tuning-hyperparameters

For use in Claude.ai and ChatGPT

Download Skill

主要功能

01Automated grid, random, and Bayesian search strategies

02Automated Python code generation for training and evaluation

03Built-in cross-validation to ensure model robustness

04Seamless integration with scikit-learn and Optuna

05Comprehensive performance reporting and metric comparison

06896 GitHub stars

使用场景

01Optimizing Random Forest or Gradient Boosting parameters for improved classification accuracy

02Minimizing error rates in regression models using Optuna-driven Bayesian optimization

03Automating the comparison of different hyperparameter ranges during model development

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills tuning-hyperparameters

For use in Claude.ai and ChatGPT

Download Skill