scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
npx skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Scikit-learn ## Overview This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines. ## Installation Tested against **scikit-learn 1.8.0** (stable; December 2025). Requires **Python 3.11–3.14** (free-threaded CPython 3.14 wheels available in 1.8+). Install the PyPI package **`scikit-learn`** (not the deprecated `sklearn` package on PyPI). Import in code as `sklearn`. ```bash # Install scikit-learn using uv uv pip install "scikit-learn>=1.7" # Optional: plotting utilities and bundled script dependencies uv pip install "scikit-learn[plots]" matplotlib seaborn # Commonly used with uv pip install pandas numpy ``` Check your version: ```python import sklearn print(sklearn.__version__) ``` ## When to Use This Skill Use the scikit-learn skill when: - Building classification or regression models - Performing clustering or dimensionality reduction - Preprocessing and transforming data for machine learning - Evaluating model performa
- Overview
- Installation
- When to Use This Skill
- Quick Start
- Classification Example
- Complete Pipeline with Mixed Data
- Core Capabilities
- Example Scripts
- Classification Pipeline
- Clustering Analysis
- Reference Documentation
- Quick Reference
- Supervised Learning
- Unsupervised Learning
Install scikit-learn using uv uv pip install "scikit-learn>=1.7" uv pip install "scikit-learn[plots]" matplotlib seaborn Commonly used with uv pip install pandas numpy uv run python scripts/classification_pipeline.py uv run python scripts/clustering_analysis.py
What does the scikit-learn skill do?
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.