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
SKILL.mdbundled/skills/splitting-datasets/SKILL.md
GitHubDataset 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
scikit-learn
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-pipelinesfor the broader preprocessing sequenceml-data-leakage-guardto verify the split does not leak future or test information