Multinomial logistic regression

"Multinomial regression" redirects here. For the related Probit procedure, see Multinomial probit.
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Background

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 polytomous LR,[2][3] multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model.[4]

  1. ^ Greene, William H. (2012). Econometric Analysis (Seventh ed.). Boston: Pearson Education. pp. 803–806. ISBN 978-0-273-75356-8.
  2. ^ Engel, J. (1988). "Polytomous logistic regression". Statistica Neerlandica. 42 (4): 233–252. doi:10.1111/j.1467-9574.1988.tb01238.x.
  3. ^ Menard, Scott (2002). Applied Logistic Regression Analysis. SAGE. p. 91. ISBN 9780761922087.
  4. ^ Malouf, Robert (2002). A comparison of algorithms for maximum entropy parameter estimation (PDF). Sixth Conf. on Natural Language Learning (CoNLL). pp. 49–55.