Multinomial logistic regression

<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, multinomial logistic regression is a <a href="/facts/Statistical_classification/jXXHRkXR">classification</a> method that generalizes <a href="/facts/Logistic_regression/mGj6mc8y">logistic regression</a> to <a href="/facts/Multiclass_classification/aS7sV9EX">multiclass problems</a>, 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 <a href="/facts/Categorical_distribution/xpc6RWM2">categorically distributed</a> <a href="/facts/Dependent_variable/dINfzIF2">dependent variable</a>, given a set of <a href="/facts/Independent_variable/dINfzIF2">independent variables</a> (which may be real-valued, binary-valued, categorical-valued, etc.).
</p><p>Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, <a href="/facts/Softmax_activation_function/pvxeWV6L">softmax</a> regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model.
</p>

Multinomial logistic regression open-in-new

Multinomial logistic regression