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Data Mining with Decision Trees (CART)

Overview

Discover the power of tree-structured data mining during this popular introductory seminar, geared toward statisticians and IT audiences who are interested in understanding the conceptual basis of decision tree technology -- what it is, why it works, how it has been used, and how it can help you make better business decisions. Explore the practical use and application of decision trees for solving real world data mining problems and learn about:

• Decision tree fundamentals
• Decision tree applications
• How to build and interpret CART models
• How to use advanced options
• alternative splitting rules
• prior probabilities
• differential costs of misclassification

Content and instructional methods

Attendees will see examples of analysis of real world data. PowerPoint slides and live modeling runs will facilitate the learning process.

Course Outline:

1. Historical Background of CART
2. Sample CART Model
   1. What is CART?
   2. How to read the tree
   3. Tree interpretation and use
3. Introducing the CART Interface
   1. Setting up the model
   2. Understanding the results
4. The Big Picture
   1. Binary recursive partitioning
   2. Workflow of a model
5. Fundamentals of Tree Pruning
6. Competitors & Surrogates
7. Variable Importance
8. Splitting Rules and Friends
   1. Introduction to splitting rules
   2. Penalizing variables
   3. Missing values
   4. Forced splits
   5. Constraints and StructuredTrees™
   6. Introduction to priors and costs
9. Cross Validation
10. Regression Trees
11. Scoring & Deployment
12. CART Automation (Batteries of runs)
13. HotSpotDetector™
14. Stable Trees & Consistency