Multi-Label Classification with Deep Learning

n machine learning, multilabel classification is a classification task where multiple labels may be assigned to each instance. This is in contrast to traditional classification, where each instance is assigned only one label. Multilabel classification is useful in cases where there are multiple possible labels for a single instance, and each label represents a different aspect or category of the data. For example, an image recognition system might be trained to recognize multiple objects in an image, such as a cat, a dog, and a person, and assign one or more labels to each image accordingly. Because each instance can have multiple labels, the output of a multilabel classification model is often represented as a binary matrix, where each column corresponds to a different label and each row corresponds to a different instance.

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