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Ridge Regularization

What is L2 regularization? Compare and contrast L2 regularization with other regularization techniques, such as L1 regularization. Explain how L2 regularization penalizes large weights in a model and prevents overfitting. Discuss the role of the regularization parameter (lambda) in controlling the impact of the regularization term on the loss function. Additionally, elaborate on scenarios or types of datasets where L2 regularization might be more beneficial or less effective compared to other regularization methods.

machine learning
Junior Level

L2 regularization, also known as Ridge regularization, is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. It works by adding a term proportional to the **square of...

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