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Early stopping is a technique that halts a model's training before it reaches the planned maximum number of epochs, when performance on validation data stops improving. It prevents overfitting, the situation where a model keeps improving on training data but its ability to generalize gets worse. It is one of the simplest and most widely used regularization techniques.
During training, a metric computed on a validation set separate from the training data is periodically monitored. When this metric stops improving for a certain number of consecutive checks, training is stopped and the checkpoint corresponding to the best-performing point is kept.
It is common practice when training many types of neural networks, including those used for classification, regression and generation tasks. It is often combined with other regularization techniques such as dropout or weight penalties, to obtain models that generalize better to unseen data.
It is an established regularization technique in machine learning practice, widely adopted for its low implementation cost and its effectiveness in preventing overfitting.
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