Early Stopping on Validation Loss or on Accuracy?

Answer: Early stopping is typically based on validation loss rather than accuracy.

Early stopping based on validation loss is generally preferred over accuracy for several reasons:

1. Generalization Performance: Validation loss is a more reliable indicator of the model’s generalization performance than accuracy. It measures how well the model is performing on unseen data, whereas accuracy can be misleading, especially in imbalanced datasets or when classes have unequal costs.
2. Sensitive to Class Distribution: Accuracy alone may not adequately capture the performance of a model, especially in scenarios where classes are imbalanced. For example, a classifier might achieve high accuracy by simply predicting the majority class, while validation loss reflects the model’s ability to make nuanced predictions across all classes.
3. Smoothness of the Optimization Landscape: Validation loss tends to have a smoother optimization landscape compared to accuracy. This smoothness can help prevent premature convergence or oscillations during training, making validation loss a more stable criterion for early stopping.
4. Early Detection of Overfitting: Validation loss typically starts increasing when the model begins to overfit, providing an early indication to stop training and prevent further deterioration in performance. In contrast, accuracy may plateau or even continue to increase slightly before sharply decreasing, leading to delayed detection of overfitting.
5. Consistency Across Models: Early stopping based on validation loss promotes consistency across different models and architectures since it focuses on optimizing the same objective function. In contrast, accuracy thresholds may vary depending on factors such as class distribution or dataset characteristics.

Conclusion:

Early stopping based on validation loss is preferred over accuracy as it provides a more reliable measure of generalization performance, is less sensitive to class distribution, has a smoother optimization landscape, facilitates early detection of overfitting, and promotes consistency across models. By monitoring validation loss during training, practitioners can effectively prevent overfitting and ensure that the model performs well on unseen data.