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Subject Item: dbr:Multinomial
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rdfs:label: Regresión logística multinomial Multinomiale logistische Regression Multinomial logistic regression
rdfs:comment: En estadística, la regresión logística multinomial generaliza el método de regresión logística para problemas multiclase, es decir, con más de dos posibles resultados discretos. Es decir, se trata de un modelo que se utiliza para predecir las probabilidades de los diferentes resultados posibles de una distribución categórica como variable dependiente, dado un conjunto de variables independientes (que pueden ser de valor real, valor binario, categórico-valorado, etc.) In der Statistik ist die multinomiale logistische Regression, auch multinomiale Logit-Regression (MNL), polytome logistische Regression, polychotome logistische Regression, Softmax-Regression oder Maximum-Entropie-Klassifikator genannt, ein regressionsanalytisches Verfahren. Sie „dient zur Schätzung von Gruppenzugehörigkeiten bzw. einer entsprechenden Wahrscheinlichkeit hierfür.“ Die Antwortvariable (auch abhängige Variable, AV) ist dabei eine nominalskalierte Variable (Unterform der kategorialen Variable, bei der die Kategorien nicht in eine sinnvolle Reihenfolge zu bringen sind). Im Falle einer ordinalskalierten AV (ebenfalls kategorial, aber in Reihenfolge mit gleichmäßigen Abständen zwischen den Kategorien zu bringen) spricht man von einer . Bei gegebener verhältnis- oder intervallskal 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. 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.).
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dbo:abstract: En estadística, la regresión logística multinomial generaliza el método de regresión logística para problemas multiclase, es decir, con más de dos posibles resultados discretos. Es decir, se trata de un modelo que se utiliza para predecir las probabilidades de los diferentes resultados posibles de una distribución categórica como variable dependiente, dado un conjunto de variables independientes (que pueden ser de valor real, valor binario, categórico-valorado, etc.) La regresión logística multinomial se conoce por una variedad de otros nombres, incluyendo regresión multiclase LR, la regresión multinomial, función SoftMax regression, Logit multinomial, clasificador de máxima entropía (MaxEnt), etc. 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. 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, multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model. In der Statistik ist die multinomiale logistische Regression, auch multinomiale Logit-Regression (MNL), polytome logistische Regression, polychotome logistische Regression, Softmax-Regression oder Maximum-Entropie-Klassifikator genannt, ein regressionsanalytisches Verfahren. Sie „dient zur Schätzung von Gruppenzugehörigkeiten bzw. einer entsprechenden Wahrscheinlichkeit hierfür.“ Die Antwortvariable (auch abhängige Variable, AV) ist dabei eine nominalskalierte Variable (Unterform der kategorialen Variable, bei der die Kategorien nicht in eine sinnvolle Reihenfolge zu bringen sind). Im Falle einer ordinalskalierten AV (ebenfalls kategorial, aber in Reihenfolge mit gleichmäßigen Abständen zwischen den Kategorien zu bringen) spricht man von einer . Bei gegebener verhältnis- oder intervallskalierter AV kann dagegen eine (Multiple) Lineare Regression gerechnet werden.
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