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dc.contributor.authorKuhn, Max-
dc.contributor.authorJohnson, Kjell-
dc.date.accessioned2021-04-20T04:35:07Z-
dc.date.available2021-04-20T04:35:07Z-
dc.date.issued2013-
dc.identifier.isbn978-1-4614-6849-3-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/104-
dc.descriptionThis work would not have been possible without the help and men- toring from many individuals, including: Walter H. Carter, Jim Garrett, Chris Gennings, Paul Harms, Chris Keefer, William Klinger, Daijin Ko, Rich Moore, David Neuhouser, David Potter, David Pyne, William Rayens, Arnold Stromberg, and Thomas Vidmar. We would also like to thank Ross Quinlan for his help with Cubist and C5.0 and vetting our descriptions of the two. At Springer, we would like to thank Marc Strauss and Hannah Bracken as well as the reviewers: Vini Bonato, Thomas Miller, Ross Quinlan, Eric Siegel, Stan Young, and an anonymous reviewer. Lastly, we would like to thank our fam- ilies for their support: Miranda Kuhn, Stefan Kuhn, Bobby Kuhn, Robert Kuhn, Karen Kuhn, and Mary Ann Kuhn; Warren and Kay Johnson; and Valerie and Truman Johnson.en_US
dc.description.abstractThis is a book on data analysis with a specific focus on the practice of predictive modeling. The term predictive modeling may stir associations such as machine learning, pattern recognition, and data mining. Indeed, these as- sociations are appropriate and the methods implied by these terms are an integral piece of the predictive modeling process. But predictive modeling encompasses much more than the tools and techniques for uncovering pat- terns within data. The practice of predictive modeling defines the process of developing a model in a way that we can understand and quantify the model’s prediction accuracy on future, yet-to-be-seen data. The entire process is the focus of this book.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectPredictive Modelingen_US
dc.subjectStatisticsen_US
dc.titleApplied Predictive Modelingen_US
dc.typeBooken_US
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