Early Stopping

Early stopping is a regularization technique used in training machine learning models, particularly in deep learning. It involves monitoring the model's performance on a validation dataset during training and halting the training process when performance begins to degrade, indicating overfitting. This method helps to prevent the model from learning noise in the training data and ensures that it generalizes better to unseen data. Early stopping is commonly employed in scenarios where training time is a concern or when the dataset is small, as it can lead to more efficient and effective model training.