In machine learning, what does the term "label" refer to?

The term "label" in machine learning refers specifically to the output variable that a model aims to predict. In supervised learning, labels are critical because they provide the correct answers or outcomes associated with the input data. For instance, in a classification problem, each data point is assigned a label that categorizes it into specific classes. This allows the model to learn from the data during training, as it uses the labels to understand how input features relate to the desired outputs.

In the context of the other choices, while some relate to elements of machine learning, they do not define what a label is. A category for feature extraction pertains to the input features used for training but does not represent the target outcome itself. The statistical method for evaluation refers to techniques used to assess model performance post-training, rather than defining labels. Finally, data visualization methods are used to present data and insights but are separate from the concept of labels in machine learning.