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.
Source: newmindsgroup/ai-agent-skills-library — distributed by TomeVault.
1---2name: scikit-learn-243description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines. Use when this capability is needed.4---56# Scikit-learn78Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.910## When to Use11- The request matches the skill description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.12- The task needs the implementation patterns, examples, validation checks, or edge cases listed in the topic map.13- The work would benefit from the complete guidance preserved in `references/full-guidance.md`.1415## Core Workflow161. Confirm the request matches this skill's trigger, scope, and risk profile.172. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.183. Load `references/full-guidance.md` when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.194. Apply only the relevant guidance instead of loading or repeating the entire reference by default.205. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.2122## Topic Map23- Overview24- Installation25- When to Use This Skill26- Quick Start27- Classification Example28- Complete Pipeline with Mixed Data29- Core Capabilities30- Supervised Learning31- Unsupervised Learning32- Model Evaluation and Selection33- Data Preprocessing34- Pipelines and Composition35- Example Scripts36- Classification Pipeline37- Clustering Analysis38- Reference Documentation39- Quick Reference40- Model Evaluation4142## Reference Map43- `references/full-guidance.md` preserves the complete original guidance, including examples and detailed edge cases.4445## Limitations46- Use this skill only when the task clearly matches the scope described above.47- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.48- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.4950## Progressive Loading51Keep this `SKILL.md` as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.5253---54> Source: [newmindsgroup/ai-agent-skills-library](https://github.com/newmindsgroup/ai-agent-skills-library) — distributed by [TomeVault](https://tomevault.io).55<!-- tomevault:4.0:skill_md:2026-05-22 -->