How does early stopping control bias and variance in deep learning models?

Early stopping prevents overfitting by halting training before variance increases significantly.

In deep learning, as training progresses, the model initially learns general patterns (reducing bias), but later starts fitting noise (increasing variance). Early stopping uses validation loss to determine the optimal stopping point. This acts as an implicit regularization technique, preventing overfitting without modifying the model architecture.

It is especially useful in deep neural networks where training dynamics are non-linear and highly sensitive to data noise.