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Unsupervised learning is used when labeled data is not available from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Unsupervised learning is a type of machine learning that is used when labeled data is not available. This means that the data used for training the model does not have predefined categories or outcomes assigned to it. In other words, the algorithm is left to find patterns and relationships on its own without the guidance of labeled data. In supervised learning, on the other hand, the algorithm is provided with a set of labeled data, which includes both input variables and the corresponding output labels. The goal of supervised learning is to learn a mapping function that can predict the output labels for new, unseen data. However, in many real-world scenarios, labeled data may not be readily available or may be too costly to obtain. In such cases, unsupervised learning becomes a valuable tool for data scientists. Unsupervised learning algorithms are able to identify h...
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