Train / Validation / Test Split Calculator

The most common split for general-purpose datasets is 70/15/15 (train/val/test) or 80/10/10. For very large datasets (100K+ samples), you can use 90/5/5 since even 5% gives thousands of examples. For small datasets (<1,000 samples), 80/20 (train/test only) with cross-validation on the training set is often preferred. The key principle: your test set must stay completely unseen until final evaluation to give an honest estimate of generalization performance.

Stratified splitting ensures each split has the same class distribution as the full dataset. For example, if 10% of your data is class A and 90% is class B, a stratified split keeps that 10/90 ratio in train, validation, and test sets. It is essential for imbalanced datasets — without it, a random split might give your test set zero examples of a rare class. Most ML libraries (scikit-learn's train_test_split with stratify=y, sklearn's StratifiedKFold) support this. Always use stratification for classification tasks unless your dataset is perfectly balanced and very large.

K-fold cross-validation divides the training data into k equal folds, trains k models (each time using k-1 folds for training and 1 fold for validation), then averages the validation metrics. It makes maximum use of limited data — every sample is used for both training and validation across different folds. Use k=5 or k=10 as defaults. K-fold is especially valuable when your dataset is small (<5,000 samples) or when you need a reliable estimate of model performance. Keep a separate held-out test set for final evaluation — never use cross-validation folds as your test set.

Random splits for time series data cause data leakage: your model trains on future data and predicts the past, which is impossible in production but makes validation metrics look artificially good. Instead, use temporal splits — train on earlier data, validate/test on later data, preserving chronological order. For example, train on months 1-8, validate on months 9-10, test on months 11-12. For rolling evaluation, use walk-forward validation: train on t=1..n, predict t=n+1, then expand the window. The gap between train and validation sets should match your real deployment lag.

As a rule of thumb: at least 30 samples per class per split for basic statistical validity. For reliable model evaluation, aim for 100+ samples per class in your validation/test sets. With fewer than 30 samples in a class per split, your metrics (accuracy, F1, AUC) will have very high variance and may not be trustworthy. For very small datasets, k-fold cross-validation is preferable to a fixed split. If you have fewer than 10 samples per class in the full dataset, consider data augmentation, transfer learning, or few-shot methods instead of traditional train/test splits.