Early Stopping

Definition

Early stopping is a regularization technique where you stop training before the model overfits.

• Instead of training for a fixed number of epochs, you monitor validation performance (loss, accuracy, AUC, etc.).
• If performance stops improving (or starts getting worse), training halts.

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Why It’s Needed

• More epochs → lower training loss, but risk of overfitting.
• Early stopping ensures you stop at the “sweet spot” → good generalization on unseen data.
• Saves computation (no wasted training epochs).

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How It Works

1. Split data → training + validation sets.
2. Train model for many epochs.
3. After each epoch:
   • Compute validation metric.
   • Track “best score.”
4. If metric hasn’t improved for N consecutive epochs (patience), stop training.

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Parameters

• Monitor → which metric to watch (validation loss, validation accuracy, AUC).
• Mode → “min” (for loss) or “max” (for accuracy/AUC).
• Patience → how many epochs to wait before stopping.
• Restore Best Weights → option to revert to weights from the best epoch.

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Example

• Model set to max 100 epochs.
• Validation loss:
  • Improves until epoch 25.
  • After epoch 25 → validation loss increases (overfitting).
• With early stopping (patience=3):
  • Training stops at epoch 28, restoring best weights from epoch 25.

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Benefits

• Prevents overfitting.
• Reduces training cost/time.
• Automatically finds near-optimal stopping point.

Drawbacks

• Needs a validation set.
• Can stop too early if metric fluctuates (patience helps avoid this).

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Summary
Early stopping = stop training when validation performance stops improving.

• Prevents overfitting and saves compute.
• Controlled by patience, monitored metric, and best weight restoration.