build-ml-pipeline

Construct machine learning pipelines using skrub DataOps graphs.

Updated Apr 19, 2026

Search Tags:#machine learning #feature engineering #data processing #sklearn #pipeline construction

Ready to run

npx skills add https://github.com/vathymut/copilot-skills --skill build-ml-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: build-ml-pipeline
Source: https://github.com/vathymut/copilot-skills/tree/main/.github/skills/build-ml-pipeline
Command: npx skills add https://github.com/vathymut/copilot-skills --skill build-ml-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires skrub, sklearn, skore, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured approach to building machine learning pipelines, ensuring they are declarative and maintainable.

Core Features & Use Cases

Declarative Pipeline Building: Utilizes skrub DataOps graphs for declarative pipeline construction.
Early X Marker Placement: Ensures the X marker is placed correctly for proper feature engineering.
Stateful vs Stateless Operations: Differentiates between stateless functions and stateful estimators.
Multi-table Inputs and Joins: Supports multi-table inputs and joins using skrub joiners.
Meta-estimators and Hyperparameter Knobs: Incorporates meta-estimators and provides hyperparameter tuning options.
Use Case: Build a machine learning pipeline for a regression task that includes feature engineering, model selection, and hyperparameter tuning.

Quick Start

build-ml-pipeline "data_dir=/path/to/data" "predict_grid=/path/to/predict_grid" "history_source=/path/to/history_source" " learner=HistGradientBoostingRegressor"

Frequently Asked Questions about build-ml-pipeline

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

FAQPage Schema

How do I build a declarative machine learning pipeline for structured data?▼

You build a machine learning pipeline by defining a skrub DataOps graph that structures data processing and feature engineering. This approach ensures your pipeline remains declarative, maintainable, and clearly structured for predictive modeling tasks.

Can I use sklearn estimators with skrub for feature engineering?▼

Yes, you can use sklearn estimators with skrub for feature engineering. The Skill differentiates between stateless functions and stateful estimators, allowing you to integrate sklearn components seamlessly within the skrub DataOps graph for predictive modeling.

What is the best way to handle multi-table inputs and joins in a machine learning pipeline?▼

The best way to handle multi-table inputs is by using skrub joiners within your machine learning pipeline. This allows you to declaratively merge multiple data sources while maintaining a structured DataOps graph for downstream feature engineering.

How do I tune hyperparameters when using skrub and skore for pipeline construction?▼

You tune hyperparameters by incorporating meta-estimators and utilizing hyperparameter knobs within the skrub DataOps graph. The skore library is then used alongside sklearn to evaluate the pipeline construction and ensure optimal model performance.

Do I need to place an X marker manually for feature engineering in sklearn pipelines?▼

Yes, you need to ensure early X marker placement for proper feature engineering. This Skill explicitly handles the correct placement of the X marker within the skrub DataOps graph to separate training inputs from targets effectively.