Multiclass classification

In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> and <a href="/facts/Statistical_classification/jXXHRkXR">statistical classification</a>, 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 <a href="/facts/Binary_classification/MeYYAMwp">binary classification</a>). For example, deciding on whether an image is showing a banana, peach, orange, or an apple is a multiclass classification problem, with four possible classes (banana, peach, orange, apple), while deciding on whether an image contains an apple or not is a binary classification problem (with the two possible classes being: apple, no apple). 
While many classification algorithms (notably <a href="/facts/Multinomial_logistic_regression/aeF5lqDE">multinomial logistic regression</a>) naturally permit the use of more than two classes, some are by nature <a href="/facts/Binary_classification/MeYYAMwp">binary</a> algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Multiclass classification should not be confused with <a href="/facts/Multi-label_classification/VQVvX0Qz">multi-label classification</a>, where multiple labels are to be predicted for each instance (e.g., predicting that an image contains both an apple and an orange, in the previous example).

Multiclass classification open-in-new

Multiclass classification