"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.

A salient and unique feature of this book is that it explicitly connects decision trees to business intelligence and elaborates on how decision trees can be used together with other data mining methods. I enjoyed the opportunity to review this very interesting book."

Huan Liu, Professor
Computer Science and Engineering
Arizona State University

"This book has changed the way I think about decision trees and will allow me to take my organization's applied analytics and business intelligence initiatives to the next level. It may sound like a cliché, but I might describe this book as providing a roadmap to everything I have wanted to accomplish using decision trees, but was afraid to try.

Armed with knowledge from de Ville and Neville, I now feel like I have much more flexibility to interactively grow a tree without apology; as opposed to simply running an algorithm and hoping the results are interpretable, relevant, and informative.

This book offers an excellent blend of history, theory, and application of decision trees, as well as a great comparison of trees with OLAP cubes and BI tools as well as regression techniques. It is a well-balanced blend of theory and application.

I would recommend this book to anyone experienced in data mining and predictive modeling, new to decision trees, or wanting more details about their specific use and implementation in SAS products."

Matt Bogard,
Market Research Coordinator, Office of Institutional Research, Western Kentucky University
Adjunct Instructor, Department of Economics, Western Kentucky University