Does it handle feature engineering?

The skill performs essential preprocessing, including data cleaning, validation, and feature extraction, to ensure the model is trained on high-quality input.

How does it evaluate model performance?

It automatically generates detailed reports featuring key metrics such as accuracy, precision, recall, and F1-score to provide a comprehensive view of model efficacy.

What types of data does this skill support?

This skill is designed for labeled datasets suitable for supervised learning, including CSVs and structured data frames containing categorical or numerical features.

Can I customize the model selection process?

Yes, while the skill automates selection based on data characteristics, you can provide specific requirements or constraints for algorithms in your natural language prompt.

ML Classification Builder

Name: ML Classification Builder
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Builds, optimizes, and evaluates robust supervised learning classification models from structured datasets using automated pipelines.

About

This skill streamlines the machine learning workflow by automating the creation and evaluation of classification models within Claude Code. By leveraging the classification-model-builder plugin, it handles complex tasks such as data preprocessing, feature selection, and hyperparameter tuning to deliver high-performance models for categorical outcomes. It is particularly useful for data scientists and developers looking to quickly prototype or deploy reliable classifiers for tasks like spam detection, customer churn prediction, and sentiment analysis while ensuring best practices in performance reporting and data validation.

Key Features

Comprehensive performance metric reporting (Accuracy, Precision, Recall, F1)
Automated model selection and training
Seamless integration with Nixtla-based TimeGPT and FastAPI services
Integrated data preprocessing and validation
Hyperparameter optimization for peak model performance
0 GitHub stars

Use Cases

Building spam detection systems from email datasets
Predicting customer churn using historical behavior data
Automating support ticket categorization based on textual content

About

Key Features

Comprehensive performance metric reporting (Accuracy, Precision, Recall, F1)
Automated model selection and training
Seamless integration with Nixtla-based TimeGPT and FastAPI services
Integrated data preprocessing and validation
Hyperparameter optimization for peak model performance
0 GitHub stars

Use Cases

Building spam detection systems from email datasets
Predicting customer churn using historical behavior data
Automating support ticket categorization based on textual content