Early Stopping
A training technique that prevents overfitting by stopping model training when performance on validation data no longer improves.
What is Early Stopping?
During training, a model may continue improving on its training data while its performance on unseen validation data begins to decline. Early stopping monitors a chosen validation metric, such as validation loss, and ends training when that metric fails to improve for a specified number of training cycles. The model can then retain the parameters from its best-performing stage.
Why is Early Stopping Important?
Training a model for too long can cause it to memorize patterns and noise in the training data rather than generalize effectively. Early stopping helps reduce this risk while also saving computational resources and training time.
Common use cases
Early stopping is commonly used in neural networks, deep learning, classification, regression, computer vision, natural language processing, and other iterative machine learning training processes.