Description: Early stopping is a regularization technique used in the training of machine learning models, especially neural networks, to prevent overfitting. It involves monitoring the model’s performance on a validation set during the training process. When it is observed that performance begins to degrade, meaning that the loss on the validation set increases or accuracy decreases, training is halted. This strategy is crucial because overfitting occurs when a model fits too closely to the training data, capturing noise and irrelevant patterns, resulting in poor performance on unseen data. Early stopping allows for a balance between model fitting and its generalization capability. Additionally, it can be easily implemented in various machine learning frameworks, where specific criteria for stopping can be set. This technique not only saves training time but also improves model efficiency by avoiding unnecessary iterations that do not contribute to better performance. In the context of deep learning, early stopping has become a standard practice, helping to optimize the learning process and obtain more robust models.
