Bhupendrashinh, thanks very much!  I ran J48 on a respondent-level data set and got a 61.75% correct classification rate!

Correctly Classified Instances         988               61.75   %
Incorrectly Classified Instances       612               38.25   %
Kappa statistic                          0.5651
Mean absolute error                      0.0432
Root mean squared error                  0.1469
Relative absolute error                 52.7086 %
Root relative squared error             72.6299 %
Coverage of cases (0.95 level)          99.6875 %
Mean rel. region size (0.95 level)      15.4915 %
Total Number of Instances             1600     

When I plot it I get an enormous chart.  Running :

>respLevelTree = J48(BRAND_NAME ~ PRI + PROM + FORM + FAMI + DRRE + FREC + MODE + SPED + REVW, data = respLevel)
>respLevelTree

...reports:

J48 pruned tree
------------------

Is there a way to further prune the tree so that I can present a chart that would fit on a single page or two?

Thanks very much in advance for any thoughts.


-Vik




On Sep 20, 2012, at 8:37 PM, Bhupendrasinh Thakre wrote:

> Not very sure what the problem is as I was not able to take your data for run. You might want to use dput() command to present the data. 
> 
> Now on the programming side. As we can see that we have more than 2 levels for the brands and hence method  = class is not able to able to understand what you actually want from it.
> 
> Suggestion : For predictions having more than 2 levels I will go for Weka and specifically C4.5 algorithm. You also have the RWeka package for it.
> 
> Best Regards,
> 
> Bhupendrasinh Thakre
> Sent from my iPhone
> 
> On Sep 20, 2012, at 9:47 PM, Vik Rubenfeld <vikr@mindspring.com> wrote:
> 
>> I'm working with some data from which a client would like to make a decision tree predicting brand preference based on inputs such as price, speed, etc.  After running the decision tree analysis using rpart, it appears that this data is not capable of predicting brand preference.  
>> 
>> Here's the data set:
>> 
>> BRND      PRI       PROM      FORM      FAMI      DRRE      FREC      MODE      SPED      REVW
>> Brand 1       0.6989    0.4731    0.7849    0.6989    0.7419    0.6022    0.8817    0.9032    0.6452
>> Brand 2       0.8621    0.3793    0.8621     0.931    0.7586    0.6897    0.8966    0.9655    0.8276
>> Brand 3          0.6       0.1       0.6       0.7       0.9       0.7       0.7       0.8       0.6
>> Brand 4       0.6429      0.25    0.5714       0.5    0.6071       0.5      0.75    0.8214       0.5
>> Brand 5       0.7586    0.4224    0.7328    0.6638    0.7328    0.6379    0.8621    0.8621    0.6897
>> Brand 6         0.75    0.0833    0.5833    0.4167       0.5    0.4167      0.75    0.6667       0.5
>> Brand 7       0.7742    0.4839    0.6129    0.5161    0.8065    0.6452    0.7742    0.9032    0.6129
>> Brand 8       0.6429    0.2679    0.6964    0.7143     0.875    0.5536    0.8036    0.9464    0.6607
>> Brand 9        0.575     0.175      0.65      0.55     0.625     0.375     0.825      0.85     0.475
>> Brand 10      0.8095    0.5238    0.6667    0.6429    0.6667    0.5952    0.8571    0.8095    0.5714
>> Brand 11      0.6308       0.3    0.6077    0.5846    0.6769    0.5231    0.7462    0.8846       0.6
>> Brand 12      0.7212    0.3152    0.7152    0.6545    0.6606     0.503    0.8061    0.8909       0.6
>> Brand 13      0.7419    0.2258    0.6129    0.5806    0.7097    0.6129     0.871    0.9677    0.3226
>> Brand 14      0.7176    0.2706    0.6353    0.5647    0.6941    0.4471    0.7176    0.9412    0.5176
>> Brand 15      0.7287    0.3437    0.5995    0.5788    0.8527    0.5478    0.8217    0.8941    0.6227
>> Brand 16         0.7       0.4       0.6       0.4         1       0.4       0.9       0.9       0.5
>> Brand 17      0.7193    0.3333    0.6667    0.6667    0.7018    0.5263    0.7719    0.8596    0.7018
>> Brand 18      0.7778    0.4127    0.6508    0.6349    0.7937    0.6032    0.8571    0.9206     0.619
>> Brand 19      0.8028    0.2817    0.6197    0.4366    0.7042    0.4366    0.7183    0.9155    0.5634
>> Brand 20      0.7736    0.2453    0.6226    0.3774    0.5849    0.3019     0.717    0.8679    0.4717
>> Brand 21      0.8481    0.2152    0.6329    0.4051    0.6329    0.4557    0.6962    0.8481    0.3418
>> Brand 22        0.75    0.3333    0.6667       0.5    0.6667    0.5833    0.9167    0.9167    0.4167
>> 
>> Here are my R commands:
>> 
>>> test.df = read.csv("test.csv")
>>> head(test.df)
>>    BRND    PRI   PROM   FORM   FAMI   DRRE   FREC   MODE   SPED   REVW
>> 1 Brand 1 0.6989 0.4731 0.7849 0.6989 0.7419 0.6022 0.8817 0.9032 0.6452
>> 2 Brand 2 0.8621 0.3793 0.8621 0.9310 0.7586 0.6897 0.8966 0.9655 0.8276
>> 3 Brand 3 0.6000 0.1000 0.6000 0.7000 0.9000 0.7000 0.7000 0.8000 0.6000
>> 4 Brand 4 0.6429 0.2500 0.5714 0.5000 0.6071 0.5000 0.7500 0.8214 0.5000
>> 5 Brand 5 0.7586 0.4224 0.7328 0.6638 0.7328 0.6379 0.8621 0.8621 0.6897
>> 6 Brand 6 0.7500 0.0833 0.5833 0.4167 0.5000 0.4167 0.7500 0.6667 0.5000
>> 
>>> testTree = rpart(BRAND~PRI  + PROM  + FORM +  FAMI+   DRRE +  FREC  + MODE +  SPED +  REVW, method="class", data=test.df)
>> 
>>> printcp(testTree)
>> 
>> Classification tree:
>> rpart(formula = BRND ~ PRI + PROM + FORM + FAMI + DRRE + FREC + 
>>   MODE + SPED + REVW, data = test.df, method = "class")
>> 
>> Variables actually used in tree construction:
>> [1] FORM
>> 
>> Root node error: 21/22 = 0.95455
>> 
>> n= 22 
>> 
>>       CP nsplit rel error xerror xstd
>> 1 0.047619      0   1.00000 1.0476    0
>> 2 0.010000      1   0.95238 1.0476    0
>> 
>> I note that only one variable (FORM) was actually used in tree construction. When I run a plot using:
>> 
>>> plot(testTree)
>>> text(testTree)
>> 
>> ...I get a tree with one branch.  
>> 
>> It looks to me like I'm doing everything right, and this data is just not capable of predicting brand preference. 
>> 
>> Am I missing anything?
>> 
>> Thanks very much in advance for any thoughts!
>> 
>> -Vik
>> 
>> 
>> 
>> 
>> 
>>   [[alternative HTML version deleted]]
>> 
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