
Data defines the model by dint of genetic programming, producing the best decile table.


Statistical and MachineLearning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition 

May 16, 2017 Forthcoming by Chapman and Hall/CRC Reference  662 Pages  200 B/W Illustrations ISBN 9781498797603  CAT# K30454
Features • One of only two books on big data on Intel's prestigous recommended reading list • Provides stepbystep solutions to common problems facing data scientists, modelers, and marketers; other books typically provide outlinedsolutions. • Illustrations involve real problems, real data, and better solutions. • Uniquely introduces two new machinelearning methods specifically tailored to database assessment of optimal model performance. • New edition will add latest methodologies as well as corresponding SAS programs.
Summary The third edition of a bestseller, Statistical and MachineLearning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machinelearning data mining. is a compilation of new and creative data mining techniques, which address the scalingup of the framework of classical and modern statistical methodology, for predictive modeling and analysis of big data. SMDM provides proper solutions to common problems facing the newly minted data scientist in the data mining discipline. Its presentation focuses on the needs of the data scientists (commonly known as statisticians, data miners and data analysts), delivering practical yet powerful, simple yet insightful quantitative techniques, most of which use the "old" statistical methodologies improved upon by the new machine learning influence.

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT1; or email at br@dmstat1.com. 
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