by Michele Laurelli
A regularization technique that stops training when validation performance stops improving.
Early stopping monitors validation loss during training and stops when it hasn't improved for a set number of epochs (patience). This prevents overfitting by stopping before the model memorizes training data.
Stop training at epoch 50 when validation loss plateaus
Patience of 10 epochs
Restore best weights
When a model learns training data too well, including noise, resulting in poor generalization to new data.
A portion of data held out during training to tune hyperparameters and prevent overfitting.
Techniques to prevent overfitting by adding constraints or penalties to the model during training.