Early Stopping

Early stopping is a regularization technique where the training of a model is halted before it reaches the point of overfitting. By monitoring the performance of the model on a separate validation set, researchers can stop the training process when the validation error starts to increase.

This prevents the model from continuing to fit the training data at the expense of generalization. It is a simple yet effective way to control model complexity and improve performance.

In the context of deep learning and iterative algorithms, it is a standard practice for ensuring robust results. It acts as a safety mechanism to prevent the model from becoming too specialized.

It is a key tool for managing the training process.