L1 regularization adds the absolute value of the coefficients to the loss function, promoting sparsity by effectively reducing some coefficients to zero. L2 regularization adds the square of the coefficients,…
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Can you explain the differences between L1 and L2 regularization and when you might choose one over the other in a machine learning model?
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