L2 Regularization, also known as Ridge Regularization, is a technique used in machine learning to prevent overfitting by adding a penalty to the loss function. This penalty is proportional to the square of the magnitude of the coefficients, which encourages the model to keep the weights small. By doing so, L2 Regularization helps to improve the generalization capability of the model on unseen data. It is commonly used in linear regression, logistic regression, and neural networks. L2 Regularization is particularly effective when dealing with multicollinearity among features, as it stabilizes the coefficient estimates.
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