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 mining, business anal...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: De Ville, Barry
Autor Corporativo: SAS Institute
Otros Autores: Neville, Padraic
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cary, N.C. : SAS Institute, 2013.
Temas:
SAS (Computer file)
Data mining > Computer programs.
Decision trees > Computer programs.
Business intelligence > Computer programs.
Forecasting > Computer programs.
Regression analysis.
Regression Analysis
Exploration de données (Informatique) > Logiciels.
Arbres de décision > Logiciels.
Prévision > Logiciels.
Analyse de régression.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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505 0 |a Decision trees : what are they? -- Descriptive, predictive, and explanatory analyses -- The mechanics of decision tree construction -- Business intelligence and decision trees -- Theoretical issues in the decision tree growing process -- The integration of decision trees with other data mining approaches. 
504 |a Includes bibliographical references (pages 233-237) and index. 
588 0 |a Print version record. 
520 8 |a 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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700 1 |a Neville, Padraic. 
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