Legend: Palais des congrès de Montréal = CC, Le Westin Montréal = W, Intercontinental Montréal = I
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_03C | Sat, 8/3/2013, 8:30 AM - 5:00 PM | W-Palais | |
| Classification and Regression Trees — Continuing Education Course | |||
| ASA | |||
| Instructor(s): Wei-Yin Loh, University of Wisconsin | |||
| In a classification or regression tree model, the data and sample space are split into two or more partitions and a simple statistical model is fitted to each of them. The resulting model is intuitive to interpret because the partitions can be displayed as a decision tree. The best algorithms can produce models with prediction accuracy at least as good as that of linear discriminant analysis and linear regression. This course reviews the major algorithms, including C4.5, CART, CHAID, GUIDE, M5, and QUEST. Also covered are ensemble procedures, such as bagging and Random forest. Besides least-squares, regression tree algorithms for quantile, Poisson, logistic, proportional hazards, multi-response, and longitudinal data are presented. Attention is paid to the strengths, weaknesses, capabilities, and limitations of each method. The methods are compared in terms of prediction accuracy, model size and interpretability, and computational requirements. Examples are drawn from business, economics, social science, biology, medicine, engineering, and sports. 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." | |||
2013 JSM Online Program Home
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