L'édition de cet ISBN n'est malheureusement plus disponible.
Afficher les exemplaires de cette édition ISBNAn 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.
Padraic Neville is a Principal Research Statistician Developer at SAS. He developed the decision tree and boosting procedures in SAS Enterprise Miner and the high-performance procedure HPFOREST. In 1984, Padraic produced the first commercial release of the Classification and Regression Trees software by Breiman, Friedman, Olshen, and Stone. He since has taught decision trees at the Joint Statistical Meetings. Neville's current research pursues better insight and prediction with multiple trees.
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