What are the applications of Lasso and Ridge regression?

What are the applications of Lasso and Ridge regression?

Lasso and Ridge regression are used in various fields for predictive modeling, especially when dealing with high-dimensional data.

Can you explain how Lasso regression works?

Lasso regression adds a penalty equal to the absolute value of the magnitude of coefficients, which helps in feature selection.

What about Ridge regression? How is it different?

Ridge regression adds a penalty equal to the square of the magnitude of coefficients, which helps in reducing multicollinearity.

In what scenarios would you prefer Lasso over Ridge?

Lasso is preferred when you want a sparse model with fewer predictors, while Ridge is better for multicollinearity without feature selection.