Validation Error

What is Validation Error?

 Validation Error refers to the error (or loss) calculated on a validation dataset during the training process of a machine learning model. The validation dataset is a subset of data that is not used for training but is used to evaluate the model's performance after each training iteration (epoch).

 

Purpose of Validation Error

• Prevent Overfitting:
  Overfitting occurs when the model learns the noise or irrelevant details in the training data, leading to poor generalization. If validation error increases while training error decreases, it is a sign of overfitting.

• Monitor Model Performance:
  Validation error helps in choosing hyperparameters like learning rate, number of layers, and nodes.

• Enable Early Stopping:
  Early stopping halts training when validation error stops decreasing, saving computation time and preventing overfitting.