What Are the Benefits of L2 Regularization in Machine Learning?
Discover how L2 regularization helps prevent overfitting and improves the performance of machine learning models by penalizing large coefficients.
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L2 regularization, also known as Ridge Regression, helps prevent overfitting in machine learning models by penalizing large coefficients. It works by adding a regularization term (the sum of the squared coefficients) to the loss function, ensuring that the model focuses on essential predictors rather than noise. This leads to improved generalization and performance on unseen data, making the model more robust and reliable.
FAQs & Answers
- What is L2 regularization in machine learning? L2 regularization, also called Ridge Regression, is a technique that adds a penalty proportional to the sum of the squared coefficients to the loss function, helping to reduce overfitting.
- How does L2 regularization prevent overfitting? It penalizes large model coefficients, encouraging smaller weights that reduce the model’s complexity and make it generalize better on unseen data.
- What is the difference between L1 and L2 regularization? L1 regularization adds a penalty equal to the absolute value of coefficients leading to sparsity, while L2 regularization penalizes the sum of squared coefficients promoting smaller but non-zero weights.
- Why is L2 regularization also called Ridge Regression? Because it was first introduced as Ridge Regression, which is a method of linear regression that applies L2 regularization to improve model stability and performance.