The basic setup of logistic regression is as follows. We are given aA dataset containingcontains ''N'' points. Each point ''i'' consists of a set of ''m'' input variables ''x''<sub>1,''i''</sub> ... ''x''<sub>''m,i''</sub> (also called [[independent variable]]s, explanatory variables, predictor variables, features, or attributes), and a [[binary variable|binary]] outcome variable ''Y''<sub>''i''</sub> (also known as a [[dependent variable]], response variable, output variable, or class), i.e. it can assume only the two possible values 0 (often meaning "no" or "failure") or 1 (often meaning "yes" or "success"). The goal of logistic regression is to use the dataset to create a predictive model of the outcome variable.
As in linear regression, the outcome variables ''Y''<sub>''i''</sub> are assumed to depend on the explanatory variables ''x''<sub>1,''i''</sub> ... ''x''<sub>''m,i''</sub>.
