Max, I installed C50. I have a question about the syntax. Per the C50 manual:
## Default S3 method:
C5.0(x, y, trials = 1, rules= FALSE,
weights = NULL,
control = C5.0Control(),
costs = NULL, ...)
## S3 method for class ’formula’
C5.0(formula, data, weights, subset,
na.action = na.pass, ...)
I believe I need the method for class 'formula'. But I don't yet see in the
manual how to tell C50 that I want to use that method. If I run:
respLevel = read.csv("Resp Level Data.csv")
respLevelTree = C5.0(BRAND_NAME ~ PRI + PROM + REVW + MODE + FORM + FAMI + DRRE
+ FREC + SPED, data = respLevel)
...I get an error message:
Error in gsub(":", ".", x, fixed = TRUE) :
input string 18 is invalid in this locale
What is the correct way to use the C5.0 method for class 'formula'?
-Vik
On Sep 21, 2012, at 4:18 AM, mxkuhn wrote:
> There is also C5.0 in the C50 package. It tends to have smaller trees that
> C4.5 and much smaller trees than J48 when there are factor predictors. Also,
> it has an optional feature selection ("winnow") step that can be used.
>
> Max
>
> On Sep 21, 2012, at 2:18 AM, Achim Zeileis <achim.zeil...@uibk.ac.at> wrote:
>
>> Hi,
>>
>> just to add a few points to the discussion:
>>
>> - rpart() is able to deal with responses with more than two classes. Setting
>> method="class" explicitly is not necessary if the response is a factor (as
>> in this case).
>>
>> - If your tree on this data is so huge that it can't even be plotted, I
>> wouldn't be surprised if it overfitted the data set. You should check for
>> this and possibly try to avoid unnecessary splits.
>>
>> - There are various ways to do so for J48 trees without variable reduction.
>> One could require a larger minimal leaf size (default is 2) or one can use
>> "reduced error pruning", see WOW("J48") for more options. They can be easily
>> used as e.g. J48(..., control = Weka_control(R = TRUE,
>> M = 10)) etc.
>>
>> - There are various other ways of fitting decision trees, see for example
>> http://CRAN.R-project.org/view=MachineLearning for an overview. In
>> particular, you might like the "partykit" package which additionally
>> provides the ctree() method and has a unified plotting interface for ctree,
>> rpart, and J48.
>>
>> hth,
>> Z
>>
>> On Thu, 20 Sep 2012, Vik Rubenfeld 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
>>>
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