👉 Stopping computation, also known as early stopping, is a technique used in machine learning to halt the training process before the model fully converges, thus preventing overfitting and saving computational resources. During training, a validation metric (such as accuracy or loss) is monitored to assess how well the model generalizes to unseen data. If the validation metric improves significantly over several epochs, it indicates that the model is starting to perform better on this validation set, suggesting that further training might lead to overfitting. Consequently, the training process is terminated early, and the model is saved based on its performance at this point. This approach helps in achieving a balance between underfitting and overfitting, optimizing the model's performance while conserving computational resources.
