Which search strategies are supported by this skill?

The skill supports Grid Search for exhaustive exploration, Random Search for speed, and Bayesian Optimization (using Optuna) for efficient searching in high-dimensional spaces.

Can it help prevent model overfitting?

Yes, the skill follows best practices by implementing cross-validation during the tuning process to ensure the selected hyperparameters generalize well to new data.

Do I need to define the search space myself?

While you can specify exact ranges, Claude can also suggest appropriate hyperparameter ranges and distributions based on the specific machine learning model you are using.

What libraries does this skill use for tuning?

It primarily generates code using industry-standard Python libraries such as scikit-learn for basic searches and Optuna for advanced Bayesian optimization.

Machine Learning Hyperparameter Tuner

Name: Machine Learning Hyperparameter Tuner
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

Optimizes machine learning model performance by automatically executing grid search, random search, and Bayesian optimization strategies.

概要

This skill empowers Claude to fine-tune machine learning models by automatically searching for optimal hyperparameter configurations. It leverages sophisticated search strategies—including Grid Search, Random Search, and Bayesian Optimization via Optuna—to explore hyperparameter spaces efficiently. By analyzing user requirements and dataset context, the skill generates executable Python code for data validation, model training, and cross-validation, ultimately reporting the best-performing settings to maximize metrics like accuracy, precision, or RMSE.

主な機能

Automatic generation of cross-validation and evaluation logic
0 GitHub stars
Automated Grid, Random, and Bayesian optimization strategies
Seamless integration with scikit-learn and Optuna libraries
Support for multiple ML model types including Random Forest and Gradient Boosting
Detailed reporting of best hyperparameter configurations and performance metrics

ユースケース

Minimizing error rates in regression tasks using Bayesian optimization
Improving the accuracy of a classification model on tabular data
Automating the complex setup of model benchmarking and tuning pipelines

概要

主な機能

Automatic generation of cross-validation and evaluation logic
0 GitHub stars
Automated Grid, Random, and Bayesian optimization strategies
Seamless integration with scikit-learn and Optuna libraries
Support for multiple ML model types including Random Forest and Gradient Boosting
Detailed reporting of best hyperparameter configurations and performance metrics

ユースケース

Minimizing error rates in regression tasks using Bayesian optimization
Improving the accuracy of a classification model on tabular data
Automating the complex setup of model benchmarking and tuning pipelines