Program-at-a-Glance
Keynote Address | Concurrent Sessions | Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demonstrations | Closing General Session with Refreshments
Keynote Address | Concurrent Sessions | Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demonstrations | Closing General Session with Refreshments
Viewing Short Course (full day)s only — View Full Program |
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Thursday, February 23 | ||
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Art and Practice of Classification and Regression Trees
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Thu, Feb 23, 8:00 AM - 5:30 PM
River Terrace 2 |
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Instructor(s): Wei-Yin Loh, University of Wisconsin
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It is more than 50 years since the first regression tree algorithm (AID, Morgan and Sonquist 1963) appeared. Rapidly increasing use of tree models among practitioners has stimulated many algorithmic advances over the last two decades. Modern tree models have higher prediction accuracy, increased computational speed, and negligible variable selection bias. They can fit linear models in the nodes using GLM, quantile, and other loss functions; response variables may be multivariate, longitudinal, or censored; and classification trees can employ linear splits and fit kernel and nearest-neighbor node models. The aims of the course are: (i) to briefly review the capabilities of the state-of-the-art methods and (ii) to show how to exploit free software to analyze data from initial data exploration to a final interpretable prediction model. Example applications include subgroup identification for precision medicine, missing value imputation, and propensity score estimation in sample surveys.
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