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Business Intelligence and Decision Modeling

Business Intelligence and Decision Modeling. Week 9 Customer Profiling Decision Trees (Part 2) CHAID CRT. CHAID or CART. Chi-Square Automatic Interaction Detector Based on Chi-Square All variables discretecized Dependent variable: nominal Classification and Regression Tree

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Business Intelligence and Decision Modeling

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  1. Business Intelligence and Decision Modeling Week 9 Customer Profiling Decision Trees (Part 2) CHAID CRT

  2. CHAID or CART • Chi-Square Automatic Interaction Detector • Based on Chi-Square • All variables discretecized • Dependent variable: nominal • Classification and Regression Tree • Variables can be discrete or continuous • Based on GINI or F-Test • Dependent variable: nominal or continuous

  3. Use of Decision Trees • Classify observations from a target binary or nominal variable Segmentation • Predictive response analysis from a target numerical variable Behaviour • Decision support rules  Processing

  4. Decision Tree

  5. Example:dmdata.sav Underlying Theory  X2

  6. CHAID AlgorithmSelecting Variables • Example • Regions (4), Gender (3, including Missing)Age (6, including Missing) • For each variable, collapse categories to maximize chi-square test of independence: Ex: Region (N, S, E, W,*)  (WSE, N*) • Select most significant variable • Go to next branch … and next level • Stop growing if …estimated X2 < theoretical X2

  7. CART (Nominal Target) • Nominal Targets: • GINI (Impurity Reduction or Entropy) Squared probability of node membership Gini=0 when targets are perfectly classified. Gini Index =1-∑pi2 • Example • Prob: Bus = 0.4, Car = 0.3, Train = 0.3 • Gini = 1 –(0.4^2 + 0.3^2 + 0.3^2) = 0.660

  8. CART (Metric Target) • Continuous Variables: Variance Reduction (F-test)

  9. Comparative Advantages(From Wikipedia) • Simple to understand and interpret • Requires little data preparation • Able to handle both numerical and categorical data • Uses a white box model easilyexplained by Boolean logic. • Possible to validate a modelusing statistical tests • Robust

  10. Where to get help? http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp

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