Skill.md
Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
SKILL.md
---
name: scikit-learn
description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
license: BSD-3-Clause license
metadata:
skill-author: K-Dense Inc.
risk: critical
source: community
date_added: "2026-09-04"
---
# 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.