Very good. Could you point me in a couple of potential directions for variable
reduction? E.g. correlation analysis?
On Sep 20, 2012, at 10:36 PM, Bhupendrasinh Thakre wrote:
> One possible way to think of it is using " variable reduction" before going
> for J48. You may want to use several methods available for that. Again
> prediction for brands is more of a business question to me.
>
> Two solution which I can think of.
> 1. Variable reduction before decision tree.
> 2. Let the intuition decide how many of them are "really" important.
>
> Please let us know your findings. All the best.
>
> Best Regards,
>
> Bhupendrasinh Thakre
> Sent from my iPhone
>
> On Sep 21, 2012, at 12:16 AM, Vik Rubenfeld <v...@mindspring.com> wrote:
>
>> 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]]
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
