What does "early stopping" achieve during model training?

Early stopping is a regularization technique used during the training of machine learning models, particularly in iterative algorithms like neural networks. The main goal of early stopping is to prevent overfitting, which occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers, leading to poor performance on unseen data.

During training, the model's performance is typically monitored on a validation set. Early stopping involves halting the training process once the model's performance on the validation set begins to degrade after improving. This is an indication that the model is starting to overfit the training data. By stopping the training at this point, the model maintains better generalization to new data, which is crucial for its performance in real-world applications.

In contrast, increasing data quality, improving model interpretability, or enhancing dataset size may impact model performance, but they do not specifically address the issue of overfitting that early stopping directly aims to mitigate. Thus, early stopping is fundamentally linked to ensuring that the model learns effectively without becoming overly complex and sensitive to the training set.