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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.
Download QR code; Print/export Download as PDF; Printable version; In other projects ... Multinomial logistic regression; O. Ordered logit; S. Separation (statistics)
Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. [6]
Download QR code; Print/export Download as PDF; Printable version; ... Logistic regression; Multinomial logistic regression; Mixed logit; Probit; Multinomial probit;
In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). For example, deciding on whether an image is showing a banana, an orange, or an ...
Multinomial distribution; Multinomial logistic regression; Multinomial logit – see Multinomial logistic regression; Multinomial probit; Multinomial test; Multiple baseline design; Multiple comparisons; Multiple correlation; Multiple correspondence analysis; Multiple discriminant analysis; Multiple-indicator kriging; Multiple Indicator Cluster ...
Due to his use of the normal distribution Thurstone was unable to generalise this binary choice into a multinomial choice framework (which required the multinomial logistic regression rather than probit link function), hence why the method languished for over 30 years. However, in the 1960s through 1980s the method was axiomatised and applied ...
The resulting model is known as logistic regression (or multinomial logistic regression in the case that K-way rather than binary values are being predicted). For the Bernoulli and binomial distributions, the parameter is a single probability, indicating the likelihood of occurrence of a single event.