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Abstract:
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This tutorial is intended for the applied statistician wanting to understand/apply CART classification and regression tree methodology. Concepts will be illustrated using real-world, step-by-step examples. The course begins with an intuitive introduction to tree-structured analysis: what it is, why it works, why it is nonparametric; model-free; and advantages in handling all types of data, including missing values and categorical. Working through examples, we will review how to read the CART Tree output and set up basic analyses. This session includes performance evaluation of CART trees and covers ways to search for improved results. Once a basic working knowledge of CART has been mastered, the tutorial will focus on critical details 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 concludes with discussion about the comparative performance of CART versus other computer-intensive methods such as neural networks and statistician-generated parametric models. Attendees receive six months access to fully functional versions of the SPM Salford Predictive Modeler software suite.
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