Calculate train, validation, and test set sizes from a dataset split ratio.
Dataset splitting divides your total samples into three sets used for different purposes: training samples update model weights, validation samples tune hyperparameters and monitor overfitting during development, and test samples give a final, untouched estimate of real-world performance. A common default is an 80/10/10 split, though larger datasets can often use a smaller validation/test percentage (e.g. 98/1/1) since even 1% of a very large dataset is statistically sufficient, while smaller datasets often need a larger validation/test share to get reliable estimates.
train_samples = round(total * train_percent / 100); val_samples = round(total * val_percent / 100); test_samples = total - train_samples - val_samples
Stratified sampling ensures each split (train/val/test) preserves the same class/label distribution as the full dataset — this is important for imbalanced datasets, since random sampling alone could leave a minority class underrepresented or entirely absent from one split.
If your entered train/val/test percentages don't sum to 100, this calculator proportionally rescales them so the resulting sample counts still add up to your total dataset size correctly.
It's a reasonable default, but very large datasets (millions+ samples) often shrink validation/test to just 1-5% each since that's still plenty of samples for reliable evaluation, freeing up more data for training; very small datasets may need k-fold cross-validation instead of a single fixed split.
The validation set is used repeatedly during development to tune hyperparameters and select the best model checkpoint, while the test set should be used only once, at the very end, to report an unbiased estimate of final model performance.