What is regularization?

Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model learns the training data too well and performs poorly on new, unseen data. It works by adding a penalty term to the loss function that discourages overly complex models, effectively simplifying the model and improving its ability to generalize. Common regularization methods include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combine L1 and L2 penalties. Dropout is another popular regularization technique used primarily in neural networks.