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, distinguishing cats from dogs)—early stopping provides minimal gains in generalization. In contrast, when a dataset contains intrinsic variability or noisy labels (such as predicting mortality among patients), early stopping becomes a critical regularization technique. In these noisy settings, training models until they completely interpolate the data is typically a bad idea, making early stopping essential to prevent overfitting.