The effectiveness of early stopping is strongly influenced by the characteristics of the training data. For realizable datasets—where classes are cleanly separable and label noise is absent (for example, perfectly clear images of cats versus dogs)—early stopping provides minimal gains in generalization. In contrast, when a dataset contains inherent ambiguity or noisy labels, early stopping becomes a critical technique to prevent overfitting.