Early stopping is a technique used in preventing overfitting and improving generalization performance of neural network models. By monitoring the model performance on a validation set during the training process, early stopping provides a mechanism to halt the training when the model starts to exhibit signs of overfitting.

Besides, early stopping can contribute to saving computational resources and time. Training neural networks can be computationally expensive, especially with large datasets and complex architectures. Early stopping enables us to avoid unnecessary training iterations, reducing the computational burden and allowing for faster experimentation and model development. This efficiency makes early stopping a practical and valuable feature for neural network training.

The SysIdentPy maintainer (wilsonrljr) is committed to helping in all steps of the implementation to make Early Stopping available for the users.