Decision Trees for Analytics Using SAS Enterprise Miner - Softcover

De Ville, Barry; Neville, Padraic

9781612903156: Decision Trees for Analytics Using SAS Enterprise Miner

Softcover

ISBN 10: 1612903150 ISBN 13: 9781612903156

Publisher: SAS Institute, 2013

Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. The book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice. Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book.

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Review:

"Decision Trees for Analytics Using SAS Enterprise Miner is an excellent book for practitioners and project managers alike. With ample figures and examples, this book clearly illustrates and explains the roles and concepts that decision trees play in descriptive, predictive, and explanatory analyses." -- Huan Liu, Professor, Computer Science and Engineering "Arizona State University"

About the Author:

Barry de Ville is a Solutions Architect at SAS. His work with decision trees has been featured during several SAS users' conferences and has led to the award of a U.S. patent on "bottom-up" decision trees. Previously, he led the development of the KnowledgeSEEKER decision tree package. He has given workshops and tutorials on decision trees at such organizations as Statistics Canada, the American Marketing Association, the IEEE, and the Direct Marketing Association.

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