ml-setup

What problem does it solve?

This Skill automates the initial setup of a machine learning project for competitions, ensuring a standardized and organized directory structure, along with essential pipeline patterns for cross-validation, metrics, and baseline models.

Core Features & Use Cases

• Project Scaffolding: Creates a canonical src/ directory layout with pre-defined modules for configuration, data handling, feature engineering, modeling, metrics, and submission.
• Pipeline Templates: Provides boilerplate code and guidance for implementing cross-validation strategies, competition-specific metrics, and baseline model approaches.
• Use Case: When starting a new ML competition, use this Skill to quickly set up a robust project structure that follows best practices, allowing you to focus immediately on feature engineering and model development.

Quick Start

Run the init.sh script from your competition's root directory to set up the project structure.