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Applied Predictive Modeling by Kjell Johnson and Max Kuhn (2018, Hardcover)

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Authors : Kuhn, Max, Johnson, Kjell. Applied Predictive Modeling. Title : Applied Predictive Modeling.

About this product

Product Identifiers

Publisher
Springer New York
ISBN-10
1461468485
ISBN-13
9781461468486
eBay Product ID (ePID)
160083956

Product Key Features

Number of Pages
Xiii, 600 Pages
Language
English
Publication Name
Applied Predictive Modeling
Subject
Biostatistics, Mathematical & Statistical Software, Probability & Statistics / General, Life Sciences / Biology
Publication Year
2018
Type
Textbook
Author
Kjell Johnson, Max Kuhn
Subject Area
Mathematics, Computers, Science, Medical
Format
Hardcover

Dimensions

Item Weight
366.9 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2013-933452
Reviews
From the book reviews: "The book under review is aimed at providing both an introduction and a practical guide of predictive modelling. ... this book is strongly recommended as a practical guide for non-mathematical readers with basic statistical knowledge. All concepts are presented within a strong practical context and are illustrated using the statistical software package R. In addition, supportive exercises are provided in each chapter." (Iris Burkholder, zbMATH 1306.62014, 2015), 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, "'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) "The book under review is aimed at providing both an introduction and a practical guide of predictive modelling. ... this book is strongly recommended as a practical guide for non-mathematical readers with basic statistical knowledge. All concepts are presented within a strong practical context and are illustrated using the statistical software package R. In addition, supportive exercises are provided in each chapter." (Iris Burkholder, zbMATH 1306.62014, 2015)
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.
Synopsis
This book provides an introduction to predictive models as well as a guide to applying them. It will serve as a useful guide for practitioners. All results can be reproduced using R., 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 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. 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.
LC Classification Number
QH323.5