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Early stopping is a method used to prevent overfitting during the training of a machine learning model. It involves monitoring the model's performance on a validation dataset and stopping the training process when the performance on this validation set begins to deteriorate or stops improving significantly. This technique is commonly used in training deep learning and other iterative models where the risk of overfitting is high.

Early stopping generally works in the way below:

• Split the data: You divide your dataset into at least two parts: one for training and one for validation. Sometimes a third, the test set, is used for final evaluation.
• Set a monitor metric: You choose a performance metric to monitor on the validation set, such as accuracy or loss.
• Training: You train the model for a number of epochs or iterations.
• Evaluation: After each epoch, evaluate the model on the validation set.
• Decision to stop: If the validation metric stops improving or starts to degrade, you stop the training. Typically, a "patience" parameter is set, allowing the model to continue training for a certain number of epochs past the initial point of degradation to ensure it's not a temporary fluctuation.
• Final model: Often, the model weights are reverted to the point where the validation performance was at its best.

Early stopping acts as a form of regularization, helping to ensure that the model generalizes well to unseen data rather than just memorizing the training set.

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