Multinomial logistic regression: Difference between revisions

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====Data points====
Specifically, it is assumed that we have a series of ''N'' observed data points. Each data point ''i'' (ranging from ''1'' to ''N'') consists of a set of ''M'' explanatory variables ''x''<sub>''1,i''</sub> ... ''x''<sub>''M,i''</sub> (also known as [[independent variable]]s, predictor variables, features, etc.), and an associated [[categorical variable|categorical]] outcome ''Y''<sub>''i''</sub> (also known as [[dependent variable]], response variable), which can take on one of ''K'' possible values. These possible values represent logically separate categories (e.g. different political parties, blood types, etc.), and are often described mathematically by arbitrarily assigning each a number from 1 to ''K''. The explanatory variables and outcome represent observed properties of the data points, and are often thought of as originating in the observations of ''N'' "experiments" — although an "experiment" may consist inof nothing more than gathering data. The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory variables and the outcome, so that the outcome of a new "experiment" can be correctly predicted for a new data point for which the explanatory variables, but not the outcome, are available. In the process, the model attempts to explain the relative effect of differing explanatory variables on the outcome.
 
Some examples: