model-selections-and-cross-validation

name	model-selections-and-cross-validation
description	[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.
version	1.0.0
tags	["data-science",["TODO"]]
metadata	{"skill-author":"Marie-Lynne Block"}

What this skill does

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

When to use it

[TODO] List concrete user intents and trigger phrases that should activate this skill.

Instructions

1. Clarify the objective, data assumptions, and success metrics.
2. Execute a leakage-safe and reproducible workflow for this skill domain.
3. Validate outputs with diagnostics, edge-case checks, and documented caveats.

Output format

• A concise plan of action
• Executable code or commands
• Validation summary with assumptions and risks

Examples

Example 1 - baseline workflow

Input: User asks for help in model-selections-and-cross-validation. Expected output: A reproducible, validated workflow using the skill's core tools.

Notes

• Prefer documented, stable APIs over experimental shortcuts.
• Record assumptions explicitly when data quality or labels are uncertain.