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andikarachman
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scikit-learn

Create reusable scikit-learn pipeline patterns for preprocessing, model selection, and evaluation.

14|2|Updated Feb 24, 2026
Search Tags:#preprocessing#pipelines#cross-validation#ml#model-evaluation#scikit-learn
One-click install
npx skills add https://github.com/andikarachman/data-science-plugin --skill scikit-learn-andikarachman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-learn
Source: https://github.com/andikarachman/data-science-plugin/tree/main/skills/scikit-learn
Command: npx skills add https://github.com/andikarachman/data-science-plugin --skill scikit-learn-andikarachman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Standardize ML model development by providing reusable scikit-learn pipeline patterns, preprocessing strategies, and evaluation guidelines to improve reproducibility and reduce boilerplate.

Core Features & Use Cases

  • Pipelines and preprocessing: ColumnTransformer-based pipelines, scalable preprocessing, and model integration.
  • Model evaluation: Cross-validation, scoring, and metrics for classification, regression, and clustering-ready patterns.
  • Reusability and collaboration: Clear templates for experiments and feature engineering aligned with sklearn conventions.

Quick Start

Create a complete training pipeline with preprocessing, a classifier, and cross-validated evaluation to compare models.

Frequently Asked Questions about scikit-learn

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a scikit-learn pipeline with preprocessing and cross-validation?▼

Build scikit-learn pipelines by combining a ColumnTransformer for scalable preprocessing with a classifier, then apply cross-validated evaluation to compare models using reusable templates that reduce boilerplate.

What is the best way to standardize ML model development and improve reproducibility?▼

Standardize ML model development by applying reusable scikit-learn pipeline patterns for preprocessing, model selection, and evaluation, which reduces boilerplate and ensures consistent, reproducible experimental workflows.

Can I use ColumnTransformer to scale preprocessing for classification and regression workflows?▼

Yes, ColumnTransformer-based pipelines enable scalable preprocessing and model integration for classification, regression, and clustering-ready patterns, aligning feature engineering with standard scikit-learn conventions.

How do I evaluate ML models using cross-validation and scoring metrics in scikit-learn?▼

Evaluate ML models using scikit-learn cross-validation, scoring, and metrics templates designed for classification, regression, and clustering, providing clear evaluation guidelines to compare model performance consistently.

Do I need numpy and pandas to design robust ML pipelines with scikit-learn patterns?▼

Yes, numpy and pandas are required dependencies to design robust scikit-learn ML pipelines, providing the foundational data structures and array operations needed for preprocessing and model integration workflows.

What are the limitations of using scikit-learn pipeline patterns for hyperparameter tuning?▼

Scikit-learn pipeline patterns provide templates for hyperparameter tuning and model selection, but are constrained to scikit-learn-compatible estimators and require adherence to sklearn conventions for reproducibility.

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