"Multinomial regression" redirects here. For the related Probit procedure, see Multinomial probit.
This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Multinomial logistic regression" – news · newspapers · books · scholar · JSTOR(November 2011) (Learn how and when to remove this message)
Part of a series on
Regression analysis
Models
Linear regression
Simple regression
Polynomial regression
General linear model
Generalized linear model
Vector generalized linear model
Discrete choice
Binomial regression
Binary regression
Logistic regression
Multinomial logistic regression
Mixed logit
Probit
Multinomial probit
Ordered logit
Ordered probit
Poisson
Multilevel model
Fixed effects
Random effects
Linear mixed-effects model
Nonlinear mixed-effects model
Nonlinear regression
Nonparametric
Semiparametric
Robust
Quantile
Isotonic
Principal components
Least angle
Local
Segmented
Errors-in-variables
Estimation
Least squares
Linear
Non-linear
Ordinary
Weighted
Generalized
Generalized estimating equation
Partial
Total
Non-negative
Ridge regression
Regularized
Least absolute deviations
Iteratively reweighted
Bayesian
Bayesian multivariate
Least-squares spectral analysis
Background
Regression validation
Mean and predicted response
Errors and residuals
Goodness of fit
Studentized residual
Gauss–Markov theorem
Mathematics portal
v
t
e
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]
^Greene, William H. (2012). Econometric Analysis (Seventh ed.). Boston: Pearson Education. pp. 803–806. ISBN 978-0-273-75356-8.
^Menard, Scott (2002). Applied Logistic Regression Analysis. SAGE. p. 91. ISBN 9780761922087.
^Malouf, Robert (2002). A comparison of algorithms for maximum entropy parameter estimation(PDF). Sixth Conf. on Natural Language Learning (CoNLL). pp. 49–55.
and 30 Related for: Multinomial logistic regression information
independent variables. In regression analysis, logisticregression (or logit regression) is estimating the parameters of a logistic model (the coefficients...
Multinomial may refer to: Multinomial theorem, and the multinomial coefficient Multinomial distribution MultinomiallogisticregressionMultinomial test...
logit model (also ordered logisticregression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent...
It is a generalization of the logistic function to multiple dimensions, and used in multinomiallogisticregression. The softmax function is often used...
Poisson regression for count data. Logisticregression and probit regression for binary data. Multinomiallogisticregression and multinomial probit regression...
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....
Bayes classifiers form a generative-discriminative pair with multinomiallogisticregression classifiers: each naive Bayes classifier can be considered...
(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance...
various other statistical models, including linear regression, logisticregression and Poisson regression. They proposed an iteratively reweighted least squares...
classification). While many classification algorithms (notably multinomiallogisticregression) naturally permit the use of more than two classes, some are...
the softmax activation function, used in multinomiallogisticregression. Another application of the logistic function is in the Rasch model, used in item...
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...
developments, including Poisson regression, ordinal logisticregression, quantile regression and multinomiallogisticregression that described by Fallah in...
regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,...
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...
analysis on categorical outcomes is accomplished through multinomiallogisticregression, multinomial probit or a related type of discrete choice model. Categorical...
evaluated as base estimators in random forests, in particular multinomiallogisticregression and naive Bayes classifiers. In cases that the relationship...
response model. As such it treats the same set of problems as does logisticregression using similar techniques. When viewed in the generalized linear model...
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding...
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable...
generalise this binary choice into a multinomial choice framework (which required the multinomiallogisticregression rather than probit link function),...
especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent...
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...
"multilevel regression" and "poststratification" ideas of MRP can be generalized. Multilevel regression can be replaced by nonparametric regression or regularized...
Global Information
