What are the applications of Lasso regularization in machine learning?

What are the applications of Lasso regularization in machine learning?

Lasso regularization is used to prevent overfitting in models, especially in high-dimensional datasets. It helps in feature selection by shrinking some coefficients to zero.

Can you give me some specific examples of where it's applied?

Sure! Lasso is commonly used in linear regression, genetics for selecting significant genes, and in finance for risk modeling.

How does it compare to Ridge regression?

Unlike Ridge regression, which shrinks coefficients but doesn't set them to zero, Lasso can completely eliminate some features, making it useful for feature selection.

Are there any limitations to using Lasso?

Yes, Lasso can be sensitive to the choice of the regularization parameter and may not perform well when features are highly correlated.