Regularization.

Regularization encompasses a family of techniques designed to prevent machine learning models from overfitting to their training data, thereby improving their ability to generalize to unseen examples. Overfitting occurs when a model learns the specific patterns and noise in the training set so well that it performs poorly on new data. Regularization methods introduce controlled constraints or modifications during training that discourage the model from memorizing training examples and instead encourage learning more robust, generalizable patterns.

Common regularization techniques include weight decay (L1 and L2 regularization), which penalizes large parameter values by adding a penalty term to the loss function, and dropout, which randomly sets some neurons to zero during training to prevent co-adaptation. Early stopping monitors validation performance and halts training when the model begins to overfit, while data augmentation artificially expands the training set with modified versions of existing examples. Batch normalization and layer normalization also provide regularizing effects by stabilizing training dynamics and reducing internal covariate shift.

The key trade-off in regularization is between model capacity and generalization: too little regularization leads to overfitting, while excessive regularization can cause underfitting where the model fails to capture important patterns. Modern deep learning relies heavily on regularization techniques, with dropout being standard in many architectures and weight decay being a universal hyperparameter. A common misconception is that regularization always improves performance, but the optimal amount depends on factors like dataset size, model complexity, and the specific task at hand.