Ridge regularization: Revision history

21 March 2023

• curprev 01:1401:14, 21 March 2023‎ Walle talk contribs‎ 3,229 bytes +3,229‎ Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, regularization is a technique used to prevent overfitting and improve the generalization of models by adding a penalty term to the objective function. Ridge regularization, also known as L2 regularization or Tikhonov regularization, is a specific type of regularization that adds a squared L2-norm of the model parameters to the loss function. This article discusses the underlying principles of ridge..."