Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google

Predicting Salary Group

A little more complex is the survey data.



> survey.rp <- rpart(Salary.Group ~ ., data=survey)
> survey.rp
n= 32561

node), split, n, loss, yval, (yprob)
      * denotes terminal node

 1) root 32561 7841 <=50K (0.75919044 0.24080956)
   2) Relationship=Not-in-family,Other-relative,Own-child,Unmarried 
                                      17800 1178 <=50K (0.93382022 0.06617978)
     4) Capital.Gain< 7073.5 17482  872 <=50K (0.95012012 0.04987988) *
     5) Capital.Gain>=7073.5 318   12 >50K (0.03773585 0.96226415) *
   3) Relationship=Husband,Wife 14761 6663 <=50K (0.54860782 0.45139218)
     6) Education=10th,11th,12th,1st-4th,5th-6th,7th-8th,9th,Assoc-acdm,
                  Assoc-voc,HS-grad,Preschool,Some-college 
                  10329 3456 <=50K (0.66540807 0.33459193)
      12) Capital.Gain< 5095.5 9807 2944 <=50K (0.69980626 0.30019374) *
      13) Capital.Gain>=5095.5 522   10 >50K (0.01915709 0.98084291) *
     7) Education=Bachelors,Doctorate,Masters,Prof-school 4432 1225 >50K
                                                     (0.27639892 0.72360108) *

> table(survey$Salary.Group)

<=50K  >50K
24720  7841



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