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Data Mining with Decision Trees (CART)

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