Whole-Sample Dataset Allocation
Allocate exact whole-sample train, validation and test counts using documented rounding, preserving the total and flagging empty splits.
Use this result well
- Inputs that matter
- Total samples, Training percentage (%), Validation percentage (%), Test percentage (%)
- Output to expect
- Train / validation / test counts
- Check the units and required inputs before comparing results.
- Keep the assumptions with a copied result so you can reproduce the calculation later.
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Reference & details
How it works
Updated September 2026
How it works
Updated September 2026Whole-sample dataset allocation
Floor each exact quota N × percent/100; assign remaining samples by descending fractional remainder, with train → validation → test as the tie order Count allocation with a documented rounding rule. No samples are selected, shuffled or stratified by this calculator. Group, person and time boundaries can take priority over exact row percentages. Preprocessing must be fitted on training data within each relevant split. No universal ratio is recommended.
Updated: September 2026
Example Scenarios
Allocate a whole dataset across three percentages using largest remainders, preserving every sample and a nonnegative count in every split.
Common Mistakes to Avoid
Common Mistakes to Avoid
Applying whole-sample dataset allocation outside its stated assumptions
No samples are selected, shuffled or stratified by this calculator. Group, person and time boundaries can take priority over exact row percentages. Preprocessing must be fitted on training data within each relevant split. No universal ratio is recommended.
FAQ
About Whole-Sample Dataset Allocation
Allocate exact whole-sample train, validation and test counts using documented rounding, preserving the total and flagging empty splits. Choose the mode that matches your measurements or study design, enter the stated units and keep the method and limits with the result.