What is a characteristic of multi-label classification?

In multi-label classification, the fundamental characteristic is that a single instance can be associated with multiple classes simultaneously. This means that instead of categorizing data points into exclusive classes, multi-label classification allows an instance to belong to multiple categories, reflecting real-world scenarios where items can possess a combination of attributes or tags. For instance, a single image can be labeled as both "sunset" and "landscape," indicating that it fits into two distinct categories at the same time.

This characteristic distinguishes multi-label classification from single-label classification, where each instance is limited to one particular class. The multi-label approach is particularly useful in various applications, such as text categorization, image tagging, and problem diagnosis in medical fields, where multiple labels can accurately describe the complexity of the instances.

The other options describe limitations or characteristics that do not align with multi-label classification, making them inaccurate in the context of this topic. For example, the assertion that a single instance is mapped to one class only describes single-label classification, while stating that the classification is limited to two outputs and requires sequential data input pertains to specific types of classification tasks that do not represent the general nature of multi-label classification.