A family of techniques that constrain or penalize model complexity during training to prevent overfitting — ensuring the model generalizes well to new, unseen data rather than memorizing the training set.
In Depth
Regularization is the practice of adding constraints to the training process that discourage the model from becoming too complex. An overly complex model can fit every quirk and noise pattern in the training data perfectly — achieving near-zero training error — while performing poorly on new data because it has memorized rather than learned. Regularization techniques force the model to find simpler, more generalizable solutions by making complexity costly.
The most common forms of regularization add a penalty term to the loss function based on the magnitude of the model's weights. L2 Regularization (Ridge) adds the sum of squared weights, which pushes all weights toward small values without eliminating any. L1 Regularization (Lasso) adds the sum of absolute weights, which drives some weights exactly to zero — effectively performing automatic feature selection. Elastic Net combines both L1 and L2 penalties. In deep learning, Dropout randomly deactivates neurons during training, forcing the network to develop redundant, robust representations.
Beyond weight penalties, other regularization strategies include Early Stopping (halting training when validation performance starts to degrade), Data Augmentation (artificially expanding the training set to expose the model to more variation), and Batch Normalization (which has an implicit regularizing effect). The appropriate amount of regularization is a hyperparameter that must be tuned — too little allows overfitting, too much causes underfitting where the model is too constrained to learn the true patterns.
Regularization prevents models from memorizing training data by penalizing complexity — it is essential for building AI systems that perform reliably on real-world, unseen data.