Description: Multi-label classification is a machine learning task where each instance can belong to multiple classes simultaneously. Unlike traditional classification, where each instance is assigned to a single class, in multi-label classification, a single input can have multiple associated labels. This is particularly relevant in contexts where categories are not mutually exclusive. For example, a photograph can be labeled with multiple tags such as ‘dog’, ‘park’, and ‘pet’ at the same time. The main characteristics of multi-label classification include the need for algorithms that can handle the correlation between labels and the evaluation of models that consider accuracy across multiple dimensions. This task has become increasingly important in data analysis, especially with the growth of unstructured information on the web and the need to classify content more accurately and contextually. Multi-label classification is used in various applications, from document categorization to content recommendation, and is fundamental in the development of artificial intelligence systems that seek to understand and organize complex information.
