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Applied Predictive Modelingby Max Kuhn
Synopses & Reviews
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.
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.
About the Author
These authors use decades of industry experience to forge a text for practitioners.
Table of Contents
General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.
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