Labeled data refers to data elements, such as images, text files or sound clips, which have been tagged with one or more labels identifying certain characteristics or classifications of the element. In the context of machine learning, these labels can represent the output or desired outcome of a predictive model based on the given data features.
In a machine learning project, labeled data sets are used to train algorithms to recognize patterns or make predictions. Each instance in the dataset includes both input data and the corresponding label, or correct answer. The machine learning model learns from these instances, progressively improving its ability to predict labels from input data. For instance, a dataset used for object recognition in images would include many images (input data) along with a label for each image noting what object it contains (the label). The trained model can then predict the object in new images based on the patterns it learned from the labeled dataset.
Labeled data can be obtained through a variety of means, including manual labeling by humans, using existing data where the 'label' is already known, or through techniques such as data augmentation where new labeled data is created based on existing data.
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