Validation Loss
Validation Loss is a measure of how well a machine learning model performs on a validation dataset that is separate from the data used to train the model.
What is Validation Loss?
Validation loss is calculated by applying the model to unseen validation data and measuring the difference between its predictions and the expected outcomes using a selected loss function. Tracking validation loss during training helps show how well the model generalizes beyond its training data.
Why is Validation Loss Important?
Validation loss helps identify issues such as overfitting and underfitting during model training. If training loss continues to decrease while validation loss begins to increase, the model may be learning the training data too closely. It is also commonly used to select models, tune hyperparameters, and determine when to stop training.
Common use cases
Validation Loss is commonly used in neural network training, hyperparameter tuning, model selection, early stopping, and machine learning model evaluation.