Early Stopping

• Early stopping watches validation loss/metric and halts training when it stops improving, and is a stopping rule driven by the validation metric's change, not a pre-fixed iteration count
• It reduces overfitting (lower variance) by not letting the model memorize noise; acts like implicit L2 regularization.

Train while checking performance on a validation set. Whenever the validation score improves, remember those weights. If it doesn't improve for a while (patience), stop and roll back to the best checkpoint. This caps model complexity at the point where it generalized best, preventing the later epochs from fitting noise.

Related Topics

• Overfitting & Underfitting - Why early stopping helps
• L1/L2 Regularization - Alternative regularization techniques
• Model Evaluation - Using validation for early stopping