What Is Regularization? An Overview of L1, L2, Dropout, and Early Stopping

Regularization adjusts the training objective, model architecture, or stopping process to curb fragile complexity and improve a model’s ability to generalize. Options include L1, L2, dropout, and early stopping. Tune their strength using validation data, and monitor fit and stability during training. Excessive regularization can erase genuine signal and lead to underfitting.