Answer / Kanchan Kumar

L1 and L2 regularization are techniques used to prevent overfitting in machine learning models. L1 regularization (also known as Lasso regression) adds a penalty term to the loss function that encourages sparse solutions, where many coefficients become zero, effectively removing redundant features from the model. L2 regularization (also known as Ridge regression) adds a quadratic penalty term to the loss function, which discourages large values in the coefficients, helping to improve the generalization performance of the model.

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