A validation dataset is a portion of data kept separate from training and used to tune a machine learning model and choose between options. It helps you pick settings (hyperparameters), compare algorithms, select features and decide when to stop training. It sits between the training dataset, which the model learns from, and the test dataset, which gives the final unbiased score.
Key takeaways
- The model does not learn directly from validation data, but your decisions do.
- It is used for tuning hyperparameters, early stopping and model selection.
- Typical splits give it 10–20% of the data.
- Cross-validation reuses data for validation when datasets are small.
A validation dataset explained in simple terms
A student preparing for exams studies from practice questions (training data). Every week they take a mock test (validation data) to see which study method works best and adjust. The real exam (test data) comes at the end, once. If the student kept seeing the real exam questions while studying, their final score would be meaningless. The mock test protects the value of the real one.
What is the validation set used for?
| Use | Example |
|---|---|
| Hyperparameter tuning | Choosing tree depth, learning rate or number of layers |
| Model selection | Comparing logistic regression, random forest and gradient boosting |
| Early stopping | Stopping training when validation loss stops improving |
| Feature selection | Keeping only features that improve validation results |
| Threshold setting | Picking the probability cut-off for a classifier |
| Spotting overfitting | Training score keeps improving while validation score gets worse |
Parameters vs hyperparameters
Parameters are learned from training data, such as a model’s weights. Hyperparameters are settings you choose before training, such as the learning rate or maximum tree depth. The validation set is how you choose hyperparameters without touching the test set.
Validation set vs test set
The validation set is used many times during development. Because you make decisions based on it, your model slowly becomes tuned to it, and its score becomes slightly optimistic. The test set is used once, at the end, to get an honest estimate. Mixing the two roles is one of the most common mistakes in machine learning.
Cross-validation
When data is limited, a single validation split can be unreliable. K-fold cross-validation solves this:
- Split the training data into k equal parts (folds), often 5 or 10.
- Train on k−1 folds and validate on the remaining fold.
- Repeat k times, so every fold is used for validation once.
- Average the scores for a more stable estimate.
Variants include stratified k-fold, which keeps class proportions balanced in each fold, and time-series split, which always validates on data later than the training data.
How to create a good validation set
- Make it represent the real data the model will see.
- For time-based problems, use later periods, not random rows.
- Keep related records together, such as all transactions from one customer, to avoid leakage.
- Make it large enough that score differences are meaningful, not noise.
Signs of problems
A big gap between training and validation scores suggests overfitting. Low scores on both suggest underfitting. Validation scores that are far better than production results suggest leakage or a validation set that does not match reality.
Limitations
Heavy tuning against one validation set can overfit to it. Small validation sets give noisy scores. And cross-validation multiplies training time. Use the validation set to guide decisions, and keep the test set untouched until the end.