New Problem: Early stopping

[moe18]

Early stopping is a regularization technique in machine learning used to prevent overfitting during training. It involves monitoring the performance of a model on a validation set during training.

If the model's performance on the validation set stops improving for a specified number of iterations (patience), training is halted, even if the model's performance on the training data continues to improve. This ensures the model doesn't overfit to the training data and maintains better generalization to unseen data.