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 <v...@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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