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 FreedomIntelligence/OpenClaw-Medical-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 ```bash # Install scikit-learn using uv uv uv pip install scikit-learn # Optional: Install visualization dependencies uv uv pip install matplotlib seaborn # Commonly used with uv uv pip install pandas numpy ``` ## 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 performance with cross-validation - Tuning hyperparameters with grid or random search - Creating ML pipelines for production workflows - Comparing different algorithms for a task - Working with both structured (tabular) and text data - Need interpretable, classical machine learning approaches ## Quick Start ### Classification Example ```python from sklearn.model_selection import train_t
- Overview
- Installation
- When to Use This Skill
- Quick Start
- Classification Example
- Complete Pipeline with Mixed Data
- Core Capabilities
- 1. Supervised Learning
- 2. Unsupervised Learning
- 3. Model Evaluation and Selection
- 4. Data Preprocessing
- 5. Pipelines and Composition
- Example Scripts
- Classification Pipeline
Install scikit-learn using uv uv uv pip install scikit-learn uv uv pip install matplotlib seaborn Commonly used with uv uv pip install pandas numpy python scripts/classification_pipeline.py 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 FreedomIntelligence/OpenClaw-Medical-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.