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In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.[1] That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.). Multinomial logistic regression is known by a variety of other names, including multiclass LR, multinomial regression,[2] softmax regression, multinomial logit, maximum entropy (MaxEnt) classifier, conditional maximum entropy model.

References: Wikipedia contributors. "Multinomial logistic regression." Wikipedia, The Free Encyclopedia, 1st April, 2015. Available at: https://en.wikipedia.org/wiki/Multinomial_logistic_regression

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