L2 regularization

A type of regularization that penalizes weights in proportion to the sum of the squares of the weights. L2 regularization helps drive outlier weights (those with high positive or low negative values) closer to 0 but not quite to 0. Features with values very close to 0 remain in the model but don't influence the model's prediction very much. L2 regularization always improves generalization in linear models. Contrast with L1 regularization. See Overfitting: L2 regularization in Machine Learning Crash Course for more information.

Practitioners refer to l2 regularization when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.