How does the skill choose between classification and regression?

The skill analyzes the target variable in your dataset; if the target is categorical, it selects classification algorithms, whereas numerical targets trigger regression models.

Do I need to clean my data before using this skill?

While the skill performs basic analysis, for the best results you should ensure your dataset is relatively clean and properly formatted before initiating the training process.

Can I use the output of this skill in a production application?

Yes, the skill saves a trained model artifact and can assist in generating FastAPI services to serve the model in a production environment.

What frameworks does this skill support?

The skill primarily supports industry-standard machine learning frameworks like scikit-learn and integrates with Nixtla for advanced time-series and forecasting pipelines.

Does it handle hyperparameter tuning?

The skill configures standard training parameters and supports optimization patterns to help find the most effective model settings for your specific data.

Machine Learning Model Trainer

Name: Machine Learning Model Trainer
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

Automates the end-to-end process of training, evaluating, and persisting machine learning models from raw datasets.

概要

This skill provides a streamlined, automated workflow for machine learning development directly within Claude Code. It handles the complexity of data analysis, target variable identification, and algorithm selection for both classification and regression tasks. By implementing cross-validation and generating detailed performance metrics, it ensures models are robust and well-evaluated before saving them as persistent artifacts. It is an essential tool for developers looking to rapidly prototype ML models, benchmark datasets, or generate production-ready scikit-learn implementations through natural language commands.

主な機能

Comprehensive cross-validation and performance evaluation
Seamless integration with scikit-learn and Nixtla workflows
Automated model type selection for classification and regression
Detailed metrics generation including Accuracy, MSE, and R-squared
Model artifact persistence for production deployment
0 GitHub stars

ユースケース

Evaluating dataset suitability for machine learning tasks
Building customer churn prediction models from CSV datasets
Automating regression analysis for real estate price forecasting

概要

主な機能

Comprehensive cross-validation and performance evaluation
Seamless integration with scikit-learn and Nixtla workflows
Automated model type selection for classification and regression
Detailed metrics generation including Accuracy, MSE, and R-squared
Model artifact persistence for production deployment
0 GitHub stars

ユースケース

Evaluating dataset suitability for machine learning tasks
Building customer churn prediction models from CSV datasets
Automating regression analysis for real estate price forecasting