Regression and Variable Selection

Regression is used for explaining the relationship between a variable Y called response, and a set of one or more variables $$X_1, X_2, \dots , X_N$$ called explanatory variables. The variable selectionVariable selection is the process that choose a reduced number of explanatory variables to describe a response variable in a regression models. Variable selectionVariable selection is used to (i) make the model easier to interpret, removing redundant non-informative variables; (ii) reduce the size of the problem to enable algorithms to run faster; and (iii) reduce the overfittingOverfitting and, consequently, make the model predictive.