In machine learning, labels or annotations are the parts of the training data set that indicate the desired ideal output of the model for a corresponding input datum for training purposes. In a broader sense, the actual outputs of a model in operation are also referred to in this way.

• ground truth
• data labelling

ETSI GR ENI 004 identification of an output value for a given input.

NOTE: Supervised learning uses labelled data; semi-supervised learning uses labels for a portion of the training data (the remaining training data are not labelled); unsupervised learning is based on training data that are not labelled.

ISO/IEC DIS 22989 the target variable assigned to a sample

Meaningful labeling of objects in raw data, for example in the form of a groundtruth, to support classification in ML to appropriately train or test an ML algorithm.

ISTQB - CTAI Syllabus The activity of adding meaningful tags to objects in raw data to support classification in ML