Scikit-learn
Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
When to Use
- The request matches the skill description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
- The task needs the implementation patterns, examples, validation checks, or edge cases listed in the topic map.
- The work would benefit from the complete guidance preserved in
references/full-guidance.md.
Core Workflow
- Confirm the request matches this skill's trigger, scope, and risk profile.
- Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
- Load
references/full-guidance.md when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
- Apply only the relevant guidance instead of loading or repeating the entire reference by default.
- Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.
Topic Map
- Overview
- Installation
- When to Use This Skill
- Quick Start
- Classification Example
- Complete Pipeline with Mixed Data
- Core Capabilities
- Supervised Learning
- Unsupervised Learning
- Model Evaluation and Selection
- Data Preprocessing
- Pipelines and Composition
- Example Scripts
- Classification Pipeline
- Clustering Analysis
- Reference Documentation
- Quick Reference
- Model Evaluation
Reference Map
references/full-guidance.md preserves the complete original guidance, including examples and detailed edge cases.
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.
Progressive Loading
Keep this SKILL.md as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.
1---2name: scikit-learn3description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.4license: MIT5---67# Scikit-learn89Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.1011## When to Use12- The request matches the skill description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.13- The task needs the implementation patterns, examples, validation checks, or edge cases listed in the topic map.14- The work would benefit from the complete guidance preserved in `references/full-guidance.md`.1516## Core Workflow171. Confirm the request matches this skill's trigger, scope, and risk profile.182. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.193. Load `references/full-guidance.md` when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.204. Apply only the relevant guidance instead of loading or repeating the entire reference by default.215. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.2223## Topic Map24- Overview25- Installation26- When to Use This Skill27- Quick Start28- Classification Example29- Complete Pipeline with Mixed Data30- Core Capabilities31- Supervised Learning32- Unsupervised Learning33- Model Evaluation and Selection34- Data Preprocessing35- Pipelines and Composition36- Example Scripts37- Classification Pipeline38- Clustering Analysis39- Reference Documentation40- Quick Reference41- Model Evaluation4243## Reference Map44- `references/full-guidance.md` preserves the complete original guidance, including examples and detailed edge cases.4546## Limitations47- Use this skill only when the task clearly matches the scope described above.48- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.49- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.5051## Progressive Loading52Keep this `SKILL.md` as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.
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