What frameworks does this skill support?

The skill primarily supports common machine learning frameworks like scikit-learn to handle various classification and regression tasks.

Is data cleaning included in this skill?

The skill performs data analysis and preparation, but for best results, it is recommended to provide a relatively clean dataset before initiating the training process.

Does it automatically choose between classification and regression?

Yes, the skill analyzes your dataset and target variable to automatically determine and implement the most appropriate model type.

How does the skill evaluate model performance?

It uses cross-validation techniques and generates detailed performance metrics such as accuracy, precision, recall, Mean Squared Error (MSE), and R-squared.

Can I save the trained model for later use?

Yes, the skill completes the workflow by saving the trained model as a persistent artifact that can be used for future predictions or production deployment.

Automated ML Training

Name: Automated ML Training
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates the end-to-end machine learning workflow by analyzing datasets, selecting optimal model types, and generating production-ready model artifacts.

About

The Automated ML Training skill empowers Claude to handle complex data science tasks by streamlining the transition from raw datasets to trained models. It automatically analyzes data structures to identify target variables, selects the most appropriate algorithms for classification or regression, and executes training with robust cross-validation. This skill is particularly useful for developers and data scientists who need to quickly prototype models, benchmark performance, or automate the generation of scikit-learn compatible model artifacts directly through natural language commands.

Key Features

Robust training using cross-validation techniques
Model artifact persistence for production deployment
0 GitHub stars
Automated data analysis and target variable identification
Intelligent selection between classification and regression models
Comprehensive performance metric generation including accuracy and MSE

Use Cases

Automating model benchmarking and performance evaluation
Building regression models for price prediction and forecasting
Training customer churn classification models from CSV datasets

About

Key Features

Robust training using cross-validation techniques
Model artifact persistence for production deployment
0 GitHub stars
Automated data analysis and target variable identification
Intelligent selection between classification and regression models
Comprehensive performance metric generation including accuracy and MSE

Use Cases

Automating model benchmarking and performance evaluation
Building regression models for price prediction and forecasting
Training customer churn classification models from CSV datasets