Regularization aims to improve prediction performance by trading an increase in training error for better agreement between training and prediction errors, which is ...

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the annual income of a person based on their sex, age, State where they live and ...

Elastic net regression uses a combination of l 1 penalty and ridge l 2 penalty and is a compromise between LASSO and ridge regression. Furthermore, one of its main advantages when p > n is that it ...