JSM 2005 Online Program

JSM Activity #CE_28T

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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CE_28T	Wed, 8/10/05, 8:00 AM - 9:45 AM	MCC-L100 C
Introduction to CART: Data Mining with Decision Trees - Continuing Education - CTW
ASA
Instructor(s): Dan Steinberg, Salford Systems, Mikhail Golovnya, Salford Systems
This Tutorial is intended for the applied statistician wanting to understand and apply the CART methodology for tree-structured non-parametric data analysis. The emphasis will be on practical data analysis involving classification. All concepts will be illustrated using real-world examples. The course will begin with an intuitive introduction to tree-structured analysis- what it is, why it works, why it is non-parametric and model-free. Working through examples, we will review how to read CART output and how to set up basic analyses. This session will include performance evaluation of CART trees and will cover ways to search for possible improvements of the results. Once a basic working knowledge of CART has been mastered, the tutorial will focus on critical details essential for advanced CART applications including: choice of splitting criteria, choosing the best split, using prior probabilities to shape results, refining results with differential misclassification costs, the meaning of cross validation, tree growing and tree pruning. The course will conclude with some discussion of the comparative performance of CART versus other computer-intensive methods such as artificial neural networks and statistician-generated parametric models. Prerequisites: This tutorial is intended to be accessible to anyone with experience with statistical modeling or data mining.
	Rejoiner: Madge Haven, American Statistical Association