What is Lasso Regression and how does it work?

What is Lasso Regression and how does it work?

Lasso Regression is a type of linear regression that includes a regularization term. It helps prevent overfitting by adding a penalty for larger coefficients.

What are the advantages of using Lasso Regression?

The main advantages include feature selection, improved prediction accuracy, and handling multicollinearity effectively.

Can you explain how the regularization term works in Lasso?

Sure! The regularization term in Lasso adds a penalty equal to the absolute value of the coefficients, which encourages sparsity in the model.

In what scenarios should I use Lasso Regression over other methods?

Lasso is particularly useful when you have a large number of features, and you suspect that many of them are irrelevant or redundant.