L2 regularization: Revision history

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18 March 2023

  • curprev 13:1213:12, 18 March 2023‎ Walle talk contribs‎ 3,193 bytes +3,193‎ Created page with "{{see also|Machine learning terms}} ==Introduction== L2 regularization, also known as ridge regression or Tikhonov regularization, is a technique employed in machine learning to prevent overfitting and improve the generalization of a model. It is a form of regularization that adds a penalty term to the objective function, which helps in constraining the model's complexity. L2 regularization is particularly useful for linear regression models, but can also be appl..."
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