Regularization reduces overfitting by adding constraints to the model. Techniques like L1 and L2 regularization penalize large weights, encouraging simpler models that generalize better. L2, also known as weight decay, is particularly common in neural networks.
Dropout is another effective method, randomly disabling neurons during training to force the network to develop redundant, robust representations. Batch normalization, while primarily used to stabilize training, also serves as a regularization technique.
Data augmentation, early stopping, and reducing model complexity are additional regularization strategies. Combining multiple methods often achieves the best balance between bias and variance.