What are the applications of regularization in machine learning?

What are the applications of regularization in machine learning?

Regularization is used to prevent overfitting in models, ensuring they generalize well to unseen data. Common applications include linear regression, logistic regression, and neural networks.

Can you explain how L1 and L2 regularization differ?

Sure! L1 regularization adds the absolute value of the coefficients as a penalty, promoting sparsity. L2 regularization adds the squared value of the coefficients, which tends to distribute the error more evenly.

In which scenarios would you prefer L1 over L2?

L1 is preferred when you suspect that many features are irrelevant, as it can reduce the number of features by setting some coefficients to zero.

Are there any specific algorithms that benefit significantly from regularization?

Yes, algorithms like Ridge Regression, Lasso Regression, and Support Vector Machines often benefit from regularization to improve performance and interpretability.