# Scikit Learn

> Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

- Skill: `francostino/scikit-learn` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add francostino/scikit-learn`
- Raw SKILL.md: https://api.skillmd.com/api/skills/francostino/scikit-learn/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: BSD-3-Clause license
- Author: FrancoStino (https://skillmd.com/u/francostino)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/francostino/scikit-learn

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# Scikit-learn

## Detailed Guide

Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

## 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

## Example Scripts

### Classification Pipeline

Run a complete classification workflow with preprocessing, model comparison, hyperparameter tuning, and evaluation:

```bash
python scripts/classification_pipeline.py
```

This script demonstrates:
- Handling mixed data types (numeric and categorical)
- Model comparison using cross-validation
- Hyperparameter tuning with GridSearchCV
- Comprehensive evaluation with multiple metrics
- Feature importance analysis

### Clustering Analysis

Perform clustering analysis with algorithm comparison and visualization:

```bash
python scripts/clustering_analysis.py
```

This script demonstrates:
- Finding optimal number of clusters (elbow method, silhouette analysis)
- Comparing multiple clustering algorithms (K-Means, DBSCAN, Agglomerative, Gaussian Mixture)
- Evaluating clustering quality without ground truth
- Visualizing results with PCA projection

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.