# Splitting Datasets

> Split datasets into training, validation, and test partitions with the right stratification and temporal rules. Use as a narrow preprocessing helper once the broader ML workflow is already chosen, not as the main route owner for an end-to-end ML task.

- Skill: `majiayu000/splitting-datasets` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/splitting-datasets`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/splitting-datasets/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- License: MIT
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/splitting-datasets

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# Dataset Splitter

## Positioning

Treat this skill as a narrow helper for partition strategy.

## When to Use

Use this skill when:
- Prepare a dataset for machine learning model training.
- Create training, validation, and testing sets.
- Partition data to evaluate model performance.

## Not For / Boundaries

- Full preprocessing-pipeline ownership: use `preprocessing-data-with-automated-pipelines`
- Leakage audits and prediction-time checks: use `ml-data-leakage-guard`
- Model training and tuning after the split: use `training-machine-learning-models`

## Typical Outputs

- Partition strategy with ratios, random seeds, and stratification rules
- Notes on temporal or grouped split constraints
- Handoff guidance for leakage review and downstream training

## Related Skills

- `preprocessing-data-with-automated-pipelines` for the broader preprocessing sequence
- `ml-data-leakage-guard` to verify the split does not leak future or test information