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Multi-class classification can be treated as an extension of
binary classification
to more than two classes. If each example can only be
assigned to one class, then the classification problem can be handled as a
binary classification problem, where one class contains one of the multiple
classes, and the other class contains all the other classes put together.
The process can then be repeated for each of the original classes.
For example, in a three-class multi-class classification problem,
where you're classifying examples with the labels A, B, and
C, you could turn the problem into two separate binary classification
problems. First, you might create a binary classifier that categorizes examples
using the label A+B and the label C. Then, you could create a second
binary classifier that reclassifies the examples that are labeled A+B
using the label A and the label B.
An example of a multi-class problem is a handwriting classifier that takes
an image of a handwritten digit and decides which digit, 0-9, is represented.
If class membership isn't exclusive, which is to say, an example can be
assigned to multiple classes, this is known as a multi-label classification
problem.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eMulti-class classification extends binary classification to handle more than two classes, often by breaking the problem down into multiple binary classifications.\u003c/p\u003e\n"],["\u003cp\u003eIn multi-class classification, each example is assigned to only one class, like classifying handwritten digits (0-9).\u003c/p\u003e\n"],["\u003cp\u003eIf an example can belong to multiple classes, it's called multi-label classification, which is a distinct but related concept.\u003c/p\u003e\n"],["\u003cp\u003eMulti-class classification can be achieved by creating a series of binary classifiers, each distinguishing between a subset of classes.\u003c/p\u003e\n"]]],[],null,["Multi-class classification can be treated as an extension of\n[**binary classification**](/machine-learning/glossary#binary_classification)\nto more than two classes. If each example can only be\nassigned to one class, then the classification problem can be handled as a\nbinary classification problem, where one class contains one of the multiple\nclasses, and the other class contains all the other classes put together.\nThe process can then be repeated for each of the original classes.\n\nFor example, in a three-class multi-class classification problem,\nwhere you're classifying examples with the labels **A** , **B** , and\n**C** , you could turn the problem into two separate binary classification\nproblems. First, you might create a binary classifier that categorizes examples\nusing the label **A+B** and the label **C** . Then, you could create a second\nbinary classifier that reclassifies the examples that are labeled **A+B**\nusing the label **A** and the label **B**.\n\nAn example of a multi-class problem is a handwriting classifier that takes\nan image of a handwritten digit and decides which digit, 0-9, is represented.\n\nIf class membership isn't exclusive, which is to say, an example can be\nassigned to multiple classes, this is known as a *multi-label* classification\nproblem.\n| Multi-class classification is explored more deeply in the [Multi-class neural networks](/machine-learning/crash-course/neural-networks/multi-class) section of the [Neural Networks](/machine-learning/crash-course/neural-networks) module.\n| **Key terms:**\n|\n- [Binary classification](/machine-learning/glossary#binary_classification) \n[Help Center](https://support.google.com/machinelearningeducation)"]]