L1 Regularization, also known as Lasso Regularization, is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty is proportional to the absolute value of the coefficients of the model parameters, encouraging sparsity in the model. As a result, some coefficients can be reduced to zero, effectively performing feature selection and simplifying the model. L1 Regularization is particularly useful in high-dimensional datasets where many features may be irrelevant or redundant. It is commonly used in linear regression, logistic regression, and other models requiring regularization.
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