How does Early Stopping improve the training process?

The concept of Early Stopping is fundamentally tied to the practice of monitoring the model's performance during training, particularly through the validation loss. By continuously assessing the validation loss, Early Stopping allows practitioners to identify when a model begins to overfit the training data. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, leading to poorer performance on unseen data.

When validation loss starts to increase while training loss continues to decrease, it indicates that the model is starting to memorize the training data rather than generalizing from it. By implementing Early Stopping, training can be halted at the point where validation loss is at its minimum, which is usually before overfitting takes place. This results in a model that generalizes better to new, unseen data, thereby improving its overall performance and robustness.

The other options, while related to neural network training, do not directly contribute to enhancing the efficacy of the training process in the same way Early Stopping does. For instance, increasing batch size impacts how gradients are calculated but does not address overfitting. Using a larger dataset can enhance learning by providing more diverse examples but is a separate strategy from employing Early Stopping. Reducing the number of layers may simplify