Basically, supervised learning is what statisticians do almost all the time. The “supervision” refers to the fact that the Y is are available, in contrast to unsupervised learning, the topic of Chapter 8, where Y is are assumed unavailable. The term “learning” is used in a heuristic sense to mean any inferential procedure that can, in principle, be tested for validity. Having measurements on Y available means that model identification, decision making, prediction, and many other goals of analysis can all be validated.
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© 2009 Springer-Verlag New York
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Clarke, B., Fokoué, E., Zhang, H.H. (2009). Supervised Learning: Partition Methods. In: Principles and Theory for Data Mining and Machine Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-98135-2_5
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DOI: https://doi.org/10.1007/978-0-387-98135-2_5
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