What is the difference between L1 and L2 regularization in Machine Learning models?

All answers to this question.

The primary difference lies in how they penalize large weights. L2 (Ridge) adds a penalty equal to the square of the magnitude of coefficients, which tends to shrink all coefficients evenly but never sets them to zero. L1 (Lasso) adds a penalty equal to the absolute value of the magnitude, which has a unique property: it can force some coefficients to exactly zero. If you have many features but suspect only a few are relevant, Lasso is your best friend because it performs automatic feature selection. In a 2024 pricing model I built, L1 allowed me to reduce 150 potential variables down to the 12 most impactful ones, making the final model much more interpretable and faster to run in production.

   Answered 2025-02-14 by Susan Thompson

Can you combine both if you have a mix of highly correlated features and also want some feature selection to occur simultaneously?

   Answered 2025-03-01 by David Martinez

• David, that is exactly what "Elastic Net" regression is for! It combines the penalties of both L1 and L2. It’s particularly useful when you have groups of correlated variables; Lasso might just pick one at random from the group, while Elastic Net will include the whole group but keep the overall model sparse. It’s a very powerful compromise for complex datasets.

     Commented 2025-03-10 by James Lee

I usually start with Ridge (L2) to see the baseline performance and then move to Lasso (L1) if I need to simplify the model for the engineering team.

   Answered 2025-03-15 by Linda Robinson

• That’s a smart iterative approach, Linda. Ridge is generally more stable when features are correlated, so it’s a great starting point before you get aggressive with feature elimination.

     Commented 2025-03-20 by Michael Scott