Multiclass classification means a classification problem where the task is to classify between more than two classes. Multilabel classification means a classification problem where we get multiple labels as output. In this article, I will explain the difference between multiclass and multilabel classification in machine learning.
Difference Between Multiclass and Multilabel Classification
Multiclass Classification:
Multiclass classification is the problem of classification in machine learning where our task is to classify between more than two classes. As in binary classification, we only classify between 2 classes in Multiclass, we classify between more than two classes.
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For example, if we classify e-mails as spam and not as spam, it is not the problem of multiclass classification because it is the problem of binary classification. But if we classify between cars like sedan, SUV and hatchback, it is the problem of multiclass classification.
Multilabel Classification:
Multilabel classification is a classification problem in machine learning where the task is to classify the labels of each instance where the labels can be from 0 to n number of classes. For example, think of a facial recognition system what to do if it recognizes multiple people in an image.
It will attach each person with a tag and then it will recognize the faces of all the people in the photo. Here, people are classes, and the recognition system will attach every tag to every class in this kind of problem.
Conclusion
In Multiclass the classes are mutually exclusive, while in Multilabel each label represents a different class. Simply put, when we classify between more than two classes, this is the problem of multiclass classification because classification between only 2 classes is a binary classification. If we assign a label to each class, then this is the problem of multilabel classification.
I hope you liked this article on the difference between Multiclass and multilabel classification in machine learning. Feel free to ask your valuable questions in the comments section below.
