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Decision Trees for Analytics Using SAS Enterprise Miner

Decision Trees for Analytics Using SAS Enterprise Miner
  

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. This book illustrates the application and operation of decision trees in business intelligence, data m... read full description below.

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ISBN 9781642953138
Barcode 9781642953138
Published 3 July 2019 by SAS Institute
Format Hardback
Alternate Format(s) View All (1 other possible title(s) available)
Author(s) By De Ville, Barry
By Neville, Padraic
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Full details for this title

ISBN-13 9781642953138
ISBN-10 164295313X
Stock Available
Status Internationally sourced; ships 6-12 working days
Publisher SAS Institute
Imprint SAS Institute
Publication Date 3 July 2019
Publication Country
Format Hardback
Author(s) By De Ville, Barry
By Neville, Padraic
Category Probability & Statistics
Mathematical & Statistical Software
Artificial Intelligence
Number of Pages 268
Dimensions Width: 191mm
Height: 235mm
Spine: 16mm
Weight 676g
Interest Age General Audience
Reading Age General Audience
NBS Text Software Packages
ONIX Text General/trade
Dewey Code 006.3
Catalogue Code Not specified

Description of this Book

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. This 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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Author's Bio

Barry de Ville is the managing director at skillfullmeans.us. Formerly, he was a Solutions Architect at SAS where his work with decision trees was featured during several SAS users' conferences and led to the award of a U.S. patent on bottom-up decision trees. Barry also 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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