Multilabel Classification

What is Multilabel Classification?

Multilabel classification is a type of supervised learning problem where an instance can belong to multiple classes simultaneously. This is different from multiclass classification, where each instance can only belong to one class. In multilabel classification, the model is trained on a dataset with multiple classes, and the output layer of the model should have as many nodes as there are classes, often using a sigmoid activation function to allow for multiple outputs.

How does Multilabel Classification work?

Multilabel classification algorithms work by learning the relationship between input features and output classes during the training phase. Once the model is trained, it can be used to predict the classes of new instances based on their input features. Some popular algorithms for multilabel classification include Binary Relevance, Classifier Chains, and Label Powerset.


A common example of multilabel classification is text classification, where a document can be assigned multiple tags or categories, such as “sports,” “politics,” and “technology.”

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