Understanding L1 and L2 Regularization in Machine Learning
Last Updated on December 9, 2025 by Editorial Team
Author(s): VARUN MISHRA
Originally published on Towards AI.
Understanding L1 and L2 Regularization in Machine Learning
Regularization is a fundamental technique in machine learning used to prevent overfitting, improve model generalization, and ensure that models perform well on unseen data. Two of the most commonly used regularization methods are L1 regularization(Lasso) and L2 regularization*(Ridge). In this article, we’ll explore what these techniques are, how they work, their differences, and when to use them.
The article discusses the concepts of L1 (Lasso) and L2 (Ridge) regularization, exploring their mathematical formulations and key characteristics. It illustrates how L1 promotes sparsity in feature selection, while L2 shrinks parameter values, making both techniques vital for mitigating overfitting in machine learning models. The comparison highlights when each method is most effective, concluding that a combined approach known as Elastic Net can leverage the strengths of both techniques for improved model performance.
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