What does "Early Stopping" mean?

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Early stopping is a technique used in training machine learning models to prevent them from becoming too complex and losing their ability to make accurate predictions. When models are trained, they can sometimes continue to improve on the training data but perform poorly on new, unseen data. This happens when they learn too much about the training data, including the noise and small details that do not help when making predictions.

To avoid this issue, early stopping monitors the model's performance on a separate set of data that it has not seen during training. If the model's performance starts to drop on this validation data after improving, training is halted before it gets too complex. This way, the model remains simple and effective at making predictions.

Using early stopping can save time and resources since it prevents unnecessary computations. It allows for quicker model selection and can improve overall performance by ensuring the model generalizes well to new data.