Validation Data
Validation data is the “middle layer” between training and test sets. While training data teaches the model and test data evaluates final performance, validation data provides ongoing feedback during training.
Why do we need it?
- Hyperparameter tuning → Adjust learning rates, batch sizes, regularization strength.
- Early stopping → Detect when the model starts overfitting and stop training at the optimal point.
- Model selection → Compare different algorithms or architectures on the same validation set.
Best practices
- Keep it separate: Validation data must not overlap with training data to avoid leakage.
- Use cross-validation: For small datasets, split the data into folds and rotate which fold is used as validation.
- Don’t peek too often: Excessive tuning on validation data can turn it into “pseudo-training data,” leading to biased evaluations.
Example
A speech recognition system trained on thousands of audio samples might use 70% of the data for training, 15% for validation, and 15% for testing. The validation set helps decide whether to use a convolutional layer, adjust dropout, or change the optimizer.
The validation set plays a bridging role: it provides feedback not on how well the model has memorized, but on how well it adapts to patterns it has never seen before. This makes it central to avoiding both underfitting and overfitting.
In practice, validation sets are often used to compare competing models or to tune hyperparameters such as learning rate, number of layers, or regularization strength. Without them, model development would be like navigating without a compass—you could train, but with little guidance on whether you are moving in the right direction.
Modern workflows extend this idea with nested cross-validation for reliable hyperparameter tuning, or with a development set in NLP benchmarks like GLUE, which acts as the “official rehearsal” before leaderboard evaluation. The key lesson: validation is not about perfection, but about ensuring that your model truly learns to generalize.
📖 References
- Stanford CS231n course notes on training/validation/test splits.
