What Validation Set Meaning, Applications & Example
A portion of training data used to evaluate model performance.
What is a Validation Set?
A Validation Set is a subset of the data used to evaluate the performance of a machine learning model during training. It helps in tuning the model’s hyperparameters and selecting the best model configuration before final testing. The validation set is distinct from the training and test sets, ensuring that the model is not evaluated on data it has already seen.
Importance of a Validation Set
- Hyperparameter Tuning : The validation set is used to adjust the model’s hyperparameters, such as learning rate or regularization strength.
- Model Selection: It helps in choosing the best model from multiple candidates by evaluating their performance on unseen data.
- Prevents Overfitting: By testing the model on a separate validation set, you can detect if the model is overfitting to the training data.
Applications of a Validation Set
- Model Tuning: Adjusting hyperparameters like the number of layers in a neural network or the maximum depth in decision trees.
- Early Stopping : Monitoring the validation set’s performance during training to stop early if the model’s performance starts to degrade, preventing overfitting.
- Cross-Validation : Splitting the dataset into multiple folds and using different subsets as validation sets to get a more robust performance estimate.
Example of Using a Validation Set
In image classification, if a model is being trained on a large dataset of images, a validation set of images that the model hasn’t seen during training is used to test how well the model generalizes to new data. If the model performs well on the validation set but poorly on the test set , it indicates that the model might have overfitted and further tuning is needed.