Early stopping is a regularization technique used in training machine learning models, especially neural networks, to prevent overfitting by halting the training process once the model performance on a validation set starts to deteriorate. By monitoring the model's performance during training, this method ensures that the model does not continue to learn noise in the training data, which can lead to poor generalization on unseen data. It acts as a safeguard that balances fitting the training data and maintaining the model's ability to perform well in practical applications.
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