l1 regularization
L1 regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the cost function that is proportional to the absolute value of the model's coefficients. This helps to enforce sparsity in the model by driving some coefficients to zero, leading to simpler and more interpretable models.
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