Answer Posted / Neetu Sagar

L1 and L2 regularization are methods used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The main difference between them lies in the nature of this penalty term: L1 regularization applies an absolute value penalty, resulting in sparse solutions with some features having zero weights. On the other hand, L2 regularization uses a squared penalty, which produces smoother models but does not necessarily result in sparse solutions.

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