**Applied Predictive Modeling 2013th Edition by Max Kuhn, ISBN-13: 978-1461468486**

[PDF eBook eTextbook]

- Publisher: Springer; 1st ed. (May 17, 2013)
- Language: English
- 613 pages
- ISBN-10: 1461468485
- ISBN-13: 978-1461468486

**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.

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

**Max Kuhn**, Ph.D., is a Senior Director in Research & Development at Pfizer in Groton, CT. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years. Dr. Kuhn has made many of contributions to statistical computing. He is the author of eight R packages for techniques in machine learning (notably *caret*) and reproducible research and is an Associate Editor for the *Journal of Statistical Software*.

**Kjell Johnson**, Ph.D, has over 15 years of predictive modeling and statistical consulting experience in pharmaceutical research and development and other industries. He is a former Director of Statistics at Pfizer R&D, and is a co-founder of Arbor Analytics, a firm that specializes in predictive modeling and statistical consulting and currently serves the pharmaceutical, medical devices, finance, and insurance industries. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

Drs. Kuhn and Johnson have taught numerous short-courses on predictive modeling for organizations such as useR!, Predictive Analytics World, Eastern North American Region, American Chemical Society, Society for Biomolecular Screening, Deming Conference, and individual corporations.

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