by Michele Laurelli
Techniques to prevent overfitting by adding constraints or penalties to the model during training.
Regularization methods include L1 (Lasso), L2 (Ridge), dropout, and early stopping. They reduce model complexity and improve generalization to new data.
L2 regularization in linear regression
Dropout in neural networks
Early stopping to prevent overtraining