Which scenarios among the following are a valid reason to use regularization?

Question

A. To drop the least useful variables of a model

B. To reduce over-fitting

C. To reduce the bias of a model

D. To decrease p-value

Answer 1

Answer: B and C - To reduce over-fitting and to reduce the bias of a model. In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems,regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.

 

Source: Wikipedia

Comments

Please provide the links to the sources as well.