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