Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
"Some important concepts are explained well including the difference between supervised and unsupervised learning, and describing when specific methods work well and when they don't. They also list twenty canonical questions and point the reader to the sections in the book where these questions are answered. They provide many important examples from biomedical research and illustrate the methods to solve these problems along with the pitfalls of some of them. ... Overall, I think this is a good reference source for biomedical researchers involved in data mining or classification, but the reader should beware of the arguments that are loosely explained."Michael R. Chernick, Significance"The book is well written and provides nice graphics and numerous applications... the book is good for its intended audience, the users of biomedical data."Michael R. Chernick, Technometrics"While biomedical applications of the statistical learning machines described in this book are becoming more apparent, they are not widely practiced. This book provides an excellent overview for the neophyte as to the nuts and bolts of certain statistical learning machines and the major issues involved in development and evaluation of specific machines. The authors display a nice dance between exuberance and caution; they do not attempt to advance any particular machine learning approach, instead emphasizing the processes and the need to use several approaches depending on data context."Wendy J. Mack, University of Southern California, Los Angeles for American Journal of Epidemiology