EarlyStopping
EarlyStopping is a training technique used in machine learning to halt the training process when the model’s performance on a validation set stops improving. By stopping at the right time, it aims to prevent overfitting to the training data and reduce unnecessary computation, while preserving or even improving generalization to unseen data.
The mechanism typically relies on a callback that evaluates a chosen validation metric after each training
Usage and considerations: EarlyStopping is prevalent in neural network training and other iterative optimization processes, often
Practical tips include selecting an appropriate validation metric for the task, using restore_best_weights to retain the