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This is the preliminary program for the 2007 Joint Statistical
Meetings in Salt Lake City, Utah.
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The views expressed here are those of the individual authors and not necessarily those of the ASA or its board, officers, or staff. Back to main JSM 2007 Program page |
= Applied Session,
= Theme Session,
= Presenter| CE_20C | Mon, 7/30/07, 8:30 AM - 5:00 PM | CC-151 A-C |
| Classification and Regression Trees - Continuing Education - Course | ||
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ASA |
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| Instructor(s): Wei-Yin Loh, University of Wisconsin-Madison | ||
| In a classification or regression tree model, the data space is split into several partitions and a simple statistical model is fitted to each partition. Because the partitioning may be displayed as a tree structure, the model is intuitive and easy to comprehend. Further, because the partitions are highly adaptive, the best algorithms are capable of prediction accuracy at least as good as that of traditional methods. This course reviews the major algorithms, including C4.5, CART, CHAID, CRUISE, GUIDE, M5, and QUEST. Also covered are ensemble procedures, such as bagging, and various types of regression, including least squares, quantile, Poisson, logistic, and relative risk regression. The emphasis is on showing the strengths and weaknesses of the methods and their capabilities compared to non-tree methods. The methods are compared in terms of prediction accuracy, model interpretability, selection bias, and computational requirements. Examples are drawn from business, economics, medicine, engineering, science, sports, and other fields. Relevant software is discussed where appropriate. Attendees should be familiar with linear regression at the level of Weisberg's Applied Linear Regression and discriminant analysis at the level of Johnson and Wichern's Applied Multivariate Statistical Analysis. | ||
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JSM 2007
For information, contact jsm@amstat.org
or phone (888) 231-3473. If you have questions about the Continuing Education program,
please contact the Education Department. |