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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 hidden patterns or structures in the data without the need for labeled examples. This can be particularly useful for tasks such as clustering, anomaly detection, and dimensionality reduction. Clustering is a common application of unsupervised learning, where the goal is to group similar data points together based on their features. This can help in identifying natural groupings in the data and understanding the underlying structure. Anomaly detection, on the other hand, involves identifying unusual or unexpected patterns in the data that may indicate potential fraud, errors, or other anomalies. Dimensionality reduction is another important use case for unsupervised learning, where the goal is to reduce the number of input variables while preserving as much information as possible. This can help in simplifying the data and making it more manageable for further analysis.- Unsupervised learning is a powerful tool for data scientists when labeled data is not available. By allowing algorithms to discover patterns and relationships in the data on their own, unsupervised learning can help in gaining valuable insights and making informed decisions in various applications.
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