Early stopping: Difference between revisions

Early stopping is a regularization technique used in machine learning to prevent overfitting. It involves monitoring the model's performance on a separate validation set during training and stopping before it begins overfitting on that data set. The goal of early stopping is that your model should perform well both on the training set and validation set so it can generalize well across new data sets.

Early stopping occurs by comparing the performance of a model on both the training set and validation set during training. The validation set is an omitted portion of data from the training set that allows us to assess its suitability for unseen data. As the model learns on this set, its performance on the validation set is monitored at regular intervals; if validation loss starts to increase, then training is stopped and the model with the best validation performance is selected as the final model.

Early stopping can be implemented using various criteria, such as monitoring validation loss or accuracy. The criteria used to terminate training can be set based on domain knowledge or determined empirically through experimentation. One popular approach involves using a patience parameter which controls how many epochs of training may continue without improvement in validation performance. If after these specified number of epochs have passed without improvement in validation performance, training is then terminated.