Early stopping is a regularization technique that halts iterative model training before performance on validation data begins to degrade. It helps reduce overfitting by selecting a training point that balances learning from data with generalization to unseen examples.
A regularization technique often used when training a machine learning model with an iterative method such as gradient descent.
A method for regularization that involves ending training before training loss finishes decreasing. In early stopping, you intentionally stop training the model when the loss on a validation dataset starts to increase; that is, when generalization performance worsens. Early stopping may seem counterintuitive. After all, telling a model to halt training while the loss is still decreasing may seem like telling a chef to stop cooking before the dessert has fully baked. However, training a model for too long can lead to overfitting. That is, if you train a model too long, the model may fit the training data so closely that the model doesn't make good predictions on new examples. Contrast with early exit.
A method for regularization that involves ending training before training loss finishes decreasing. In early stopping, you intentionally stop training the model when the loss on a validation dataset starts to increase; that is, when generalization performance worsens.