Multi-class classification refers to those classification tasks that have more than two class labels.
Unlike binary classification, multi-class classification does not have the notion of normal and abnormal outcomes. Instead, examples are classified as belonging to one among a range of known classes.
The number of class labels may be very large on some problems. For example, a model may predict a photo as belonging to one among thousands or tens of thousands of faces in a face recognition system.
Problems that involve predicting a sequence of words, such as text translation models, may also be considered a special type of multi-class classification. Each word in the sequence of words to be predicted involves a multi-class classification where the size of the vocabulary defines the number of possible classes that may be predicted and could be tens or hundreds of thousands of words in size.
- https://machinelearningmastery.com/types-of-classification-in-machine-learning
- https://medium.com/@b.terryjack/tips-and-tricks-for-multi-class-classification-c184ae1c8ffc
- https://towardsdatascience.com/multi-class-classification-one-vs-all-one-vs-one-94daed32a87b (use incognito)
- http://www.37steps.com/exam/mclassification/html/mclassification.html