Applied Predictive Modeling
ISBN-13:
9781461468486
ISBN-10:
1461468485
Edition:
2013
Author:
Max Kuhn, Kjell Johnson
Publication date:
2013
Publisher:
Springer
Format:
Hardcover
613 pages
Category:
Mathematical & Statistical
,
Software
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Book details
ISBN-13:
9781461468486
ISBN-10:
1461468485
Edition:
2013
Author:
Max Kuhn, Kjell Johnson
Publication date:
2013
Publisher:
Springer
Format:
Hardcover
613 pages
Category:
Mathematical & Statistical
,
Software
Summary
Applied Predictive Modeling (ISBN-13: 9781461468486 and ISBN-10: 1461468485), written by authors
Max Kuhn, Kjell Johnson, was published by Springer in 2013.
With an overall rating of 4.0 stars, it's a notable title among other
Mathematical & Statistical
(Software) books. You can easily purchase or rent Applied Predictive Modeling (Hardcover, Used) from BooksRun,
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Description
Winner of the 2014 Technometrics Ziegel Prize for Outstanding Book
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.
The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.
This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, 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 uses data available in the book's R package.
Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.
The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.
This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, 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 uses data available in the book's R package.
Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.
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