early stopping
Early stopping is a method used in machine learning to prevent overfitting by stopping the training process before the model becomes too complex and starts to memorize the training data. This is done by monitoring the model's performance on a separate validation set and stopping the training when the performance starts to degrade. By doing this, early stopping helps to find the optimal balance between model complexity and generalization.
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