A method for regularization that involves ending model training before training loss finishes decreasing. In early stopping, you end model training when the loss on a validation dataset starts to increase.
Card 1 of 259. Answer: A method for regularization that involves ending model training before training loss finishes decreasing. In early stopping, you end model training when the loss on a validation dataset starts to increase.
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Early stopping is a regularization method that ends training once performance on a held-out validation set stops improving, not when the training loss stops falling. Training loss keeps dropping as the model memorizes noise, but validation loss turns back up; early stopping keeps the model from near that turn.
Validation loss is noisy, so you wait a set number of epochs, the patience. Suppose it reads 0.52, 0.41, 0.36, 0.34, 0.35, 0.33, 0.34, 0.36 and 0.37 over nine epochs. Stopping at the first rise, epoch 5, would miss the better epoch 6. With a patience of 3, training ends after epoch 9 and the epoch 6 weights are restored.
Interviewers ask why it counts as regularization: it limits how far the weights move from their small initial values, and for a quadratic loss trained by gradient descent it acts much like an L2 penalty. The epoch count is now tuned on validation data, so report results on a test set. Keras's EarlyStopping keeps the last weights unless restore_best_weights=True.
