Decision Trees for Analytics Using SAS Enterprise Miner
By Barry de Ville and Padraic Neville
Anticipated publication date: Second quarter 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 Analytics 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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