Early stopping is a form of regularization technique used in machine learning and deep learning to avoid overfitting of the model on the training data. Overfitting happens when a model learns not only underlying patterns but also the noise in the training data, which results in poor performance on unseen or validation data. Early stopping combats overfitting by stopping the training process before the model starts to overfit.
How Early Stopping works
During the training process in machine learning and deep learning, the model's performance is continually evaluated on a separate validation set. At the beginning of training, the model's error on the validation set typically decreases. However, after a certain number of epochs, the model starts to overfit, and this is usually signaled by an increase in validation error.
With early stopping, training is stopped as soon as the validation error begins to increase. This means that we do not continue training up to the point where the model starts overfitting, hence the term “early stopping”. The model state (such as weights and biases in a neural network) at the point with the lowest validation error is usually saved and considered the best model.
In practical implementation, to avoid stopping training too early due to random fluctuations in validation error, a patience parameter could be set. This determines the number of epochs where the validation error is allowed to increase or remain stagnant before training is stopped. The patience helps to ensure that training is stopped due to genuine overfitting, and not due to noise or random fluctuations.
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