L2 regularization, or Ridge regression, is a technique to prevent overfitting in machine learning by adding a penalty proportional to the sum of squared weights to the loss function. It forces weights to be small but rarely zero, resulting in a smoother, more stable model. Solver
☆17Mar 17, 2026Updated 7 months ago
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