Multinomial logistic regression is a binary logistic regression extension that can handle more than two dependent or outcome variables. It is similar to logistic regression, except that there are many possible outcomes rather than just one. It is a traditional supervised machine learning approach with multi-class classification capabilities. The multinomial logistic model includes various assumptions, one of which is that data is thought to be case-specific, meaning that each independent variable has a single value for each instance. The multinomial logistic model also posits that in any given scenario, the dependent variable cannot be precisely predicted from the independent variables.