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About Applied Predictive Modeling
Product 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. Review "There are a wide variety of books available on predictive analytics and data modeling around the web. We've 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) "I used this as a supplement in teaching a data science course that 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) "This is such a good book it has taken me a while to work through the book. All the while finding examples of why people should read the book. Well thought out examples with the R packages and example code. Take your time and work through this book." (Mary Anne, Cats and Dogs with Data, maryannedata.com, February, 2015) "This monograph presents a very friendly, practical course on prediction techniques for regression and classification models. The authors are recognized experts in modeling and forecasting, as well as developers of R packages and statistical methodologies. 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) "There are hundreds of books that have something worthwhile to say about predictive modeling. However, 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 stuff. They write simply and with great clarity. 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) Review "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 W
