The Concept
Early Stopping is a regularization technique that stops training when the model's performance on validation data starts to deteriorate, preventing overfitting.
Key Idea:
• Monitor validation loss during training
• When validation loss stops improving (or gets worse), stop training
• Use the best model from earlier in training
• Prevents the model from overfitting to training data
Why It Works: Training loss usually keeps decreasing, but validation loss may start increasing when overfitting begins.
Implementation Strategies
🕐 Patience Strategy
Wait for a certain number of epochs without improvement before stopping
📊 Min Delta Strategy
Only consider improvements above a minimum threshold significant
💾 Best Model Restoration
Save the best model and restore it when stopping early
📈 Monitoring Metrics
Choose appropriate metrics to monitor (loss, accuracy, F1-score, etc.)