Applied Predictive Modeling by Max Kuhn (English) Hardcover Book

Description: Applied Predictive Modeling by Max Kuhn, Kjell Johnson Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. 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. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioners 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 books R package.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. Notes Book specializes in data analysis with focus on practice of predictive modelingUseful as a guide for practitionersReader can reproduce all results using R Back Cover 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. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. 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 books 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 practitioners 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 books 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. Author Biography Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Table of Contents General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices. Review "…In teaching a data science course…I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods. The next time I teach this course, I will use only this book because it covers all of these aspects of the field." (Louis Luangkesorn, lugerpitt.blogspot.com, June 2015)"There are a wide variety of books available on predictive analytics and data modeling around the web…weve carefully selected the following 10 books, based on relevance, popularity, online ratings, and their ability to add value to your business. 1. Applied Predictive Modeling." (Timothy King, Business Intelligence Solutions Review, solutions-review.com, June 2015)"Applied Predictive Modeling aims to expose many of these techniques in a very readable and self-contained book. This is a very applied and hands-on book. It guides the reader through many examples that serve to illustrate main points, and it raises possible issues and considerations that are oftentimes overlooked or not sufficiently reflected upon. Highly recommended." (Bojan Tunguz, tunguzreview.com, June 2015)"This monograph presents a very friendly practical course on prediction techniques for regression and classification models… It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques." (Stan Lipovetsky, Technometrics, Vol. 56 (3), August 2014)"In my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list …They come across like coaches who really, really want you to be able to do this…Applied Predictive Modeling is a remarkable text…it is the succinct distillation of years of experience of two expert modelers…" (Joseph Rickert, blog.revolutionanalytics.com, June 2014) Promotional "This strong, technical, hands-on treatment clearly spells out the concepts, and illustrates its themes tangibly with the language R, the most popular open source analytics solution." (Eric Siegel, Ph.D. Founder, Predictive Analytics World, Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die) Review Quote This strong, technical, hands-on treatment clearly spells out the concepts, and illustrates its themes tangibly with the language R, the most popular open source analytics solution. Eric Siegel , Ph.D. Founder, Predictive Analytics World, Author, Feature Book specializes in data analysis with focus on practice of predictive modeling Useful as a guide for practitioners Reader can reproduce all results using R Details ISBN1461468485 Author Kjell Johnson Language English ISBN-10 1461468485 ISBN-13 9781461468486 Format Hardcover Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States Short Title APPLIED PREDICTIVE MODELING 20 Media Book DEWEY 519.5 Pages 600 Edition 1st DOI 10.1007/978-1-4614-6849-3 UK Release Date 2018-03-30 AU Release Date 2018-03-30 NZ Release Date 2018-03-30 Publisher Springer-Verlag New York Inc. Edition Description 1st ed. 2013, Corr. 2nd printing 2018 Alternative 9781493979363 Audience Professional & Vocational Year 2013 Publication Date 2013-05-17 Illustrations 296 Illustrations, color; 14 Illustrations, black and white; XIII, 600 p. 310 illus., 296 illus. in color. 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