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Applied Predictive ModelingBOOK DETAILHardcover: 613 pages Publisher: Springer; 1st ed. 2013, Corr. 2nd printing 2018 edition (May 17, 2013)Language: English ISBN-10: 1461468485 ISBN-13: 978-1461468486 Product Dimensions: 6.1 x 1.3 x 9.2inches Shipping Weight: 2.2 pounds (View shipping rates and policies) Customer Reviews: 4.4 out of 5stars127 customer ratingsStep By Step To Download Or Read Online1. Click Button "DOWNLOAD" Or "READ ONLINE"2. Sign Up To Acces "Applied Predictive Modeling"3. Choose the book you like when you register4. You can also cancel your membership if you are bored5. Enjoy and Happy Reading
Book DescriptionWinner of the 2014 Technometrics Ziegel Prize for Outstanding Book Applied PredictiveModeling covers the overall predictive modeling process, beginning with the crucial steps of datapreprocessing, data splitting and foundations of model tuning. The text then provides intuitiveexplanations of numerous common and modern regression and classification techniques, alwayswith an emphasis on illustrating and solving real data problems. Addressing practical concernsextends beyond model fitting to topics such as handling class imbalance, selecting predictors, andpinpointing causes of poor model performance―all of which are problems that occur frequentlyin practice. The text illustrates all parts of the modeling process through many hands-on, real-lifeexamples. And every chapter contains extensive R code for each step of the process. The datasets and corresponding code are available in the book's companion AppliedPredictiveModeling Rpackage, which is freely available on the CRAN archive. This multi-purpose text can be used asan introduction to predictive models and the overall modeling process, a practitioner's referencehandbook, or as a text for advanced undergraduate or graduate level predictive modeling courses.To that end, each chapter contains problem sets to help solidify the covered concepts and usesdata available in the book's R package. Readers and students interested in implementing themethods should have some basic knowledge of R. And a handful of the more advanced topicsrequire some mathematical knowledge.
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