Table of Contents
Why We Split Data
When you train a model, it can appear to perform well simply because it has adapted to the specific examples it has already seen. To estimate how well it will behave on new, unseen data, you must evaluate it on data that was not used to update the model’s weights. This is the purpose of splitting your dataset into separate parts, typically training and validation, and later a test set in a different chapter.
The training set is used to compute gradients and update parameters. The validation set is used to choose decisions such as model size, number of epochs, learning rate, and other settings, without letting the model directly learn from those validation targets.
Train vs Validation Sets
A practical way to think about the split is that training answers, “How do I fit parameters?” and validation answers, “Which training choices generalize best?” In PyTorch terms, the training set is fed through the training loop with backpropagation, while the validation set is run under evaluation conditions to compute metrics only.
Never use validation data to perform gradient updates. If you train on the validation set, even indirectly, your validation metric stops being an honest estimate of generalization.
Common Split Ratios and When to Use Them
A common default is 80 percent training and 20 percent validation. If your dataset is small, you may want a larger validation fraction to reduce noise in your estimate, but that also reduces training data, which can hurt learning. If your dataset is large, you can often use 90 percent training and 10 percent validation because even 10 percent may still be many examples.
Whatever ratio you choose, the key is consistency across experiments so that comparisons are meaningful.
Random Splits and Reproducibility
A random split is the usual starting point. Randomness must be controlled so your results are reproducible. If the split changes every run, metrics can move simply due to different examples ending up in validation.
In practice, you choose a random seed and use it consistently when splitting. In PyTorch, torch.utils.data.random_split accepts a generator so you can make the split repeatable.
If you change the split between experiments, you are not doing a fair comparison, because you also changed the evaluation set.
Stratified Splits for Classification
For classification problems, especially when classes are imbalanced, a purely random split can accidentally produce a validation set with very few examples of a rare class. That makes validation metrics unstable and can mislead you into thinking the model is better or worse than it is.
A stratified split keeps the class proportions similar across training and validation. PyTorch does not provide a built-in stratified split utility for arbitrary datasets, so beginners often use scikit-learn’s train_test_split(..., stratify=labels) to get indices, then wrap those indices with torch.utils.data.Subset. The main idea is that the split should preserve label distribution.
Avoiding Data Leakage
Data leakage happens when information from validation examples influences training, causing overly optimistic validation metrics. Leakage can be obvious, like accidentally training on the full dataset, but it can also be subtle.
A common leakage source is preprocessing. Any transformation that learns from data, such as computing normalization statistics like mean and standard deviation, must be fit using training data only, then applied to validation. If you compute normalization using the entire dataset, validation data has influenced the training pipeline.
Any preprocessing step that uses dataset statistics must be computed on the training split only. Applying full-dataset statistics is a form of leakage.
Another frequent leakage case is duplicates or near duplicates. If the same or almost the same example appears in both splits, validation becomes artificially easy.
Grouped Splits for Correlated Samples
Sometimes your samples are not independent. For example, you might have multiple rows per user, multiple frames from the same video, multiple measurements from the same patient, or multiple images of the same object under slightly different conditions. If you randomly split individual rows, you can place related samples into both training and validation, again making validation overly optimistic.
In these cases you want a grouped split: all examples from the same group must go entirely to training or entirely to validation. The implementation detail depends on how your dataset stores group identifiers, but the rule is simple: split by group, not by row.
Time Based Splits for Time Series and Forecasting
If your data has a time order and you are trying to predict future behavior, random splitting can leak future information into training. A time based split uses earlier data for training and later data for validation, which matches the real use case.
Even if you are not doing explicit forecasting, any scenario where time correlates with the target can be sensitive to this. The validation set should reflect how the model will be used.
How Splits Interact with DataLoaders
Once you have training and validation subsets, you typically create separate DataLoaders. The training DataLoader usually shuffles, because shuffling helps stochastic optimization. The validation DataLoader usually does not shuffle, because shuffling is unnecessary for evaluation and you often want stable, repeatable ordering.
Batch size can be the same for both, but it is also common to use a larger batch size for validation to make evaluation faster, since you are not backpropagating.
A Minimal, Reproducible PyTorch Split
A straightforward approach with a PyTorch dataset object is to use random_split with a seeded generator. Conceptually, you will compute sizes, create the splits, then build two DataLoaders. The crucial pieces are that the split is deterministic and that you treat the two splits differently during training and evaluation.
If you shuffle before splitting using operations that change dataset order without a fixed seed, you can accidentally create non reproducible splits even if you later set a seed.
What to Watch for When Reading Validation Metrics
Validation metrics are estimates, not guarantees. If your validation set is small, metrics will have higher variance, meaning they can jump around between epochs and between experiments. This does not necessarily mean training is broken.
Also remember that you will likely look at validation many times while tuning decisions. That repeated tuning can make validation performance optimistic over time, which is one reason a separate test set exists, covered elsewhere.
Summary of Split Rules You Should Follow
The training split is for gradient based learning, the validation split is for selection and monitoring. Keep the split fixed across experiments, use stratification when class balance matters, avoid leakage by fitting preprocessing only on training data, and use grouped or time based splits when samples are correlated or ordered. These practices make your validation results meaningful, which is essential before you start tuning models and interpreting learning curves.