Scikit-learn Reference

What it does

This skill provides a complete, practical reference for using scikit-learn to build, evaluate, and deploy classical machine learning models. It bundles clear guidance and example scripts for supervised learning (classification/regression), unsupervised learning (clustering/dimensionality reduction), preprocessing, model evaluation, hyperparameter tuning, and production-ready pipelines. The content includes end-to-end examples, recommended pipelines, and reference documents for common algorithms and metrics.

When to use it

Use this skill when you need to: train or compare classical ML algorithms; construct reproducible pipelines that include preprocessing and encoding; evaluate models with robust cross-validation strategies; tune hyperparameters; or prepare models for production. It is useful for data scientists prototyping models, engineers operationalising pipelines, and learners needing concise, copy-pasteable templates.

What's included

• Scripts: classification_pipeline.py, clustering_analysis.py and example scripts for pipeline construction (has_scripts=true).
• References: comprehensive markdown docs for supervised_learning, unsupervised_learning, preprocessing, model_evaluation, and pipelines (has_references=true).
• Instructions: quick-start examples, best-practice notes (use pipelines, stratified splits), troubleshooting, and recommended metrics.

Compatible agents

Works well as a knowledge/reference skill for code-capable agents (Copilot/Codex/Claude Code/Gemini-like agents) that can run or suggest Python examples.