Early Stopping
Early stopping is a regularization technique used in machine learning to prevent overfitting by halting the training process when model performance on a validation set stops improving. It monitors metrics like validation loss or accuracy during training epochs and stops training once performance plateaus or degrades, saving computational resources and time. This method helps achieve a balance between underfitting and overfitting by selecting an optimal stopping point.
Developers should use early stopping when training deep learning models, neural networks, or any iterative machine learning algorithms prone to overfitting, such as in image classification or natural language processing tasks. It is particularly valuable in scenarios with limited data or complex models, as it automatically determines the best number of training epochs without manual tuning, improving generalization to unseen data.