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March 19, 2026 · 1 min read

Can you explain the differences between L1 and L2 regularization and when you might choose one over the other in a machine learning model?

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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📅 Mar 19, 2026 ⏱ 1 min read

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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?

COVER // CAN YOU EXPLAIN THE DIFFERENCES BETWEEN L1 AND L2 REGULARIZATION AND WHEN YOU MIGHT CHOOSE ONE OVER THE OTHER IN A MACHINE LEARNING MODEL?

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, which shrinks all coefficients but rarely sets them to zero, helping to prevent overfitting without eliminating features entirely.

data-science l1 l2 machine learning regularization

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