Decision Trees for Analytics Using SAS Enterprise Miner
| Author | : | |
| Rating | : | 4.59 (511 Votes) |
| Asin | : | 1612903150 |
| Format Type | : | paperback |
| Number of Pages | : | 272 Pages |
| Publish Date | : | 2016-05-25 |
| Language | : | English |
DESCRIPTION:
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. 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. "Decision Trees for Analytics Using SAS Enterprise Miner is an excellent book for practitioners and project managers alike. 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 tr
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.. 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. 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. 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. 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. This book illustrates the application and oper
High Level Commentary on Decision Trees This book is written by two knowledgeable and experienced professionals versed in Decision Trees applications and technology. There is considerable information about the approach's history, strengths, limitations, and variations. It treats with balance the variety of technical choices and approaches to be considered in adopting decision tree technology. It provides wisdom and is a fine book to have if you are going to adopt the approach for . "A very good position about decision trees" according to Henryk J. Runka. A very good position about decision trees. On illustrated decision trees information on nodes seem to be like on earlier versions of SAS EM than SAS EM 12.2 or later.
Previously, he led the development of the KnowledgeSEEKER decision tree package. Padraic Neville is a Principal Research Statistician Developer at SAS. patent on "bottom-up" decision trees. His work with decision trees has been featured during several SAS users' conferences and has led to the award of a U.S. He developed the decision tree and boosting procedures in SAS Enterpri