Data Science Numerical Analysis
L2 regularization, also known as Ridge regression, is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function. This penalty is proportional to the square of the magnitude of the coefficients, which discourages the model from fitting too closely to the training data. By doing so, L2 regularization helps improve the generalization of models, particularly in contexts involving large datasets or complex features.
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