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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データサイエンスとML

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

概要

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.

主な機能

3 GitHub stars
Automated Python code generation for Scikit-learn and Optuna
Integrated cross-validation to prevent model overfitting
Smart detection of optimal search spaces and model requirements
Supports Grid, Random, and Bayesian optimization strategies
Comprehensive performance metric reporting and analysis

ユースケース

Fine-tuning a Random Forest classifier for improved accuracy on tabular data
Optimizing Gradient Boosting regressors using Bayesian techniques to minimize RMSE
Comparing different search strategies to find the most efficient path to model optimization

概要

主な機能

3 GitHub stars
Automated Python code generation for Scikit-learn and Optuna
Integrated cross-validation to prevent model overfitting
Smart detection of optimal search spaces and model requirements
Supports Grid, Random, and Bayesian optimization strategies
Comprehensive performance metric reporting and analysis

ユースケース

Fine-tuning a Random Forest classifier for improved accuracy on tabular data
Optimizing Gradient Boosting regressors using Bayesian techniques to minimize RMSE
Comparing different search strategies to find the most efficient path to model optimization