A machine learning branch that learns from unlabeled test data, lacking explicit categorization or classification. Unlike supervised learning, unsupervised learning does not rely on labeled feedback. Instead, it identifies patterns and commonalities within the data, enabling it to react based on the presence or absence of such patterns in new data. This approach is valuable for tasks where labeled data is scarce or unavailable. For instance, Jaxon utilizes unsupervised learning to create topic labels, leveraging its ability to discern underlying structures and themes within unstructured data without explicit guidance.
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