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AI & Machine Learning · Intermediate

L2 Regularization

Adds a penalty proportional to the squared weights, shrinking all weights toward zero (Ridge, weight decay).

Definition

L2 regularization discourages large weights without forcing them to zero, handling correlated features well. In linear regression it is called Ridge; in neural networks it is closely related to weight decay.

Exam tip

If the question wants to keep all features but reduce overfitting, choose L2.

Related terms