Early stopping: a regularization technique to prevent overfitting

Early stopping is a machine learning technique that halts model training when performance on unseen data begins to degrade, preventing overfitting. This method monitors a model's performance on a validation set, stopping training and retaining the best-performing weights when validation metrics worsen, thus improving generalization. It's a widely used, efficient regularization method that saves computational resources but requires careful parameter tuning to avoid underfitting or overfitting the validation set itself.