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Labeled data is a designation for pieces of data that have been tagged with one or more labels identifying certain properties or characteristics, or classifications or contained objects. Labels make that data specifically useful in certain types of machine learning known as supervised machine learning setups.
In supervised machine learning, labeled data acts as the orientation for data training and testing exercises. The supervised machine learning program may start out with a set of entirely labeled data, or it may use initial labeled data to work with additional unlabeled data.
Supervised machine learning works like this – the program looks at the labeled data and makes corresponding comparisons and analysis. For example, by plotting various labeled categories on a scatter graph, the machine learning program can help determine whether successive items fall into one category or another. The algorithms use the labeled data as fodder for decision-making paradigms. This is in contrast to a different type of machine learning called unsupervised machine learning where unlabeled data is used. In unsupervised machine learning, the machine learning program has to evaluate data without labels, according to its natural properties and characteristics.