Calculator Inputs
Example Data Table
| Total Samples | Test % | Validation % | Train Count | Test Count | Validation Count |
|---|---|---|---|---|---|
| 1000 | 20 | 10 | 700 | 200 | 100 |
| 850 | 15 | 15 | 596 | 128 | 126 |
| 320 | 25 | 5 | 224 | 80 | 16 |
Formula Used
Raw test count = Total Samples × (Test Percentage ÷ 100)
Raw validation count = Total Samples × (Validation Percentage ÷ 100)
Train count = Total Samples − Test Count − Validation Count
Effective subset percentage = Subset Count ÷ Total Samples × 100
The calculator first computes raw decimal counts. It then applies your chosen rounding rule. Any rounding overshoot is trimmed from holdout sets so the final counts still sum to the original dataset size.
How to Use This Calculator
- Enter the total number of records in your dataset.
- Set the desired test percentage.
- Add a validation percentage if you need model tuning.
- Enter class count when you want balanced class estimates.
- Choose shuffle, stratification, and rounding preferences.
- Click Calculate Split to view results above the form.
- Use the CSV or PDF buttons to download the report.
Frequently Asked Questions
1. What does a train test split do?
It separates a dataset into training, testing, and sometimes validation subsets. This helps measure how well a model generalizes to unseen data rather than memorizing the original examples.
2. Why is validation different from testing?
Validation supports tuning decisions during development. Testing is normally reserved for the final unbiased evaluation after model choices, thresholds, and hyperparameters have already been decided.
3. When should I use stratified sampling?
Use it when classes are imbalanced or when preserving label proportions matters. Stratification helps each subset reflect the overall class distribution more reliably.
4. Why do rounded counts sometimes change percentages?
Percentages often produce decimal counts. After rounding, the final integers may differ slightly from the requested shares, especially with small datasets or several subsets.
5. Is 80 20 always the best split?
No. Good split choices depend on dataset size, class balance, noise, and tuning needs. Smaller datasets may need cross validation in addition to one holdout test set.
6. Should I shuffle before splitting?
Usually yes, especially when records are ordered by time, class, or source. Shuffling reduces the risk that one subset captures only one segment of the data.
7. What does the random seed control?
A random seed fixes the shuffling sequence. Using the same seed later makes your split reproducible, which helps debugging, reporting, and collaboration.
8. Can this calculator replace cross validation?
No. It estimates one holdout split configuration. Cross validation evaluates several folds and often gives a more stable performance estimate on limited datasets.