Scikit-learn (Classical ML reference)

What it does

Provides an end-to-end reference and runnable scripts for classical machine learning using scikit-learn. Covers supervised and unsupervised algorithms, preprocessing, model evaluation, hyperparameter tuning, and pipelines with practical examples and scripts for classification, clustering, and production workflows.

When to use it

Use this skill when you need to build or evaluate classical ML models (classification/regression), compare algorithms, create reproducible pipelines, tune hyperparameters, or preprocess tabular/text data for modeling. It's suited for data exploration, model selection, and preparing models for production.

What's included

• Scripts: classification_pipeline.py, clustering_analysis.py and other example scripts (has_scripts=true)
• References: extensive markdown reference files for supervised_learning, unsupervised_learning, preprocessing, model_evaluation, pipelines (has_references=true)
• Instructions: quick-start examples, preprocessing patterns, pipeline templates, evaluation strategies, and troubleshooting guidance summarized from the SKILL.md body.

Compatible agents

Best used by agents with Python execution and code-reading capabilities (Copilot-style or Claude Code / Codex-like agents), and any agent that can run prepared scripts and interpret outputs.