refactor:scikit-learn
CommunityTurn ML code into production-ready pipelines.
Data & Analytics#machine learning#pipeline#refactor#reproducibility#cross-validation#scikit-learn#ColumnTransformer
AuthorSnakeO
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill converts fragmented ML scripts into cohesive, production-ready pipelines, eliminating data leakage risks and improving reproducibility across experiments.
Core Features & Use Cases
- Pipeline-first design: encapsulates preprocessing and modeling in a single Pipeline to prevent leakage.
- ColumnTransformer & Custom Transformers: handles heterogeneous data and customizable feature engineering while maintaining API compatibility.
- Robust evaluation patterns: enforces proper cross-validation, fixed random_state, and systematic hyperparameter tuning for reliable comparisons.
Quick Start
Refactor the provided scikit-learn script to implement a Pipeline with a ColumnTransformer, validate with cross-validation, and set deterministic random_state. Then run the refactored code on your dataset and compare results to the original workflow.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: refactor:scikit-learn Download link: https://github.com/SnakeO/claude-debug-and-refactor-skills-plugin/archive/main.zip#refactor-scikit-learn Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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