Early Stopping
Early stopping is a technique used in machine learning to prevent overfitting of a model. It involves monitoring the performance of the model on a validation set during the training process and stopping the training when the performance on the validation set starts to degrade. This is done to prevent the model from becoming too complex and fitting to noise in the training data, which can lead to poor generalization performance on new data. Early stopping can be implemented by setting a threshold on the validation loss or by monitoring other metrics such as accuracy or F1 score. It is a widely used technique in deep learning, where models can have millions of parameters and are prone to overfitting.
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