Multinomial logistic regression: Difference between revisions

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In [[statistics]], '''multinomial logistic regression''' is a [[statistical classification|classification]] method that generalizes [[logistic regression]] to [[multiclass classification|multiclass problems]], i.e. with more than two possible discrete outcomes.<ref>{{cite book |last=Greene |first=William H. |authorlinkauthor-link=William Greene (economist) |title=Econometric Analysis |edition=Seventh |___location=Boston |publisher=Pearson Education |year=2012 |isbn=978-0-273-75356-8 |pages=803–806 }}</ref> That is, it is a model that is used to predict the probabilities of the different possible outcomes of a [[categorical distribution|categorically distributed]] [[dependent variable]], given a set of [[independent variable]]s (which may be real-valued, binary-valued, categorical-valued, etc.).

Multinomial logistic regression is known by a variety of other names, including '''polytomous LR''',<ref>{{Cite journal | doi = 10.1111/j.1467-9574.1988.tb01238.x| title = Polytomous logistic regression| journal = Statistica Neerlandica| volume = 42| issue = 4| pages = 233–252| year = 1988| last1 = Engel | first1 = J.}}</ref><ref>{{cite book |title=Applied Logistic Regression Analysis |url=https://archive.org/details/appliedlogisticr00mena |url-access=limited |first=Scott |last=Menard |publisher=SAGE |year=2002 |page=[https://archive.org/details/appliedlogisticr00mena/page/n99 91]}}</ref> '''multiclass LR''', '''[[Softmax activation function|softmax]] regression''', '''multinomial logit''' ('''mlogit'''), the '''maximum entropy''' ('''MaxEnt''') classifier, and the '''conditional maximum entropy model'''.<ref name="malouf">{{cite conference |first=Robert |last=Malouf |year=2002 |url=http://aclweb.org/anthology/W/W02/W02-2018.pdf |title=A comparison of algorithms for maximum entropy parameter estimation |conference=Sixth Conf. on Natural Language Learning (CoNLL) |pages=49–55}}</ref>