Thanks Max,



I have been able to figure out the following options so far: 

1. The winnow = TRUE option in the control statement

2. CF = . I have no clue as to how this works

3. nGlobalPruning = TRUE

4. minCases = 



Only the 4th one is simple to understand. The rest are a bit vague as to how they work. I was actually looking for an option to collapse a specific branch in the tree - like we had in the SPSS Answer Tree program - where we could visually collapse a node if it was not really useful.



Another question - have you compared the decision trees of C5.0 against CRUISE, GUIDE, LOTUS and QUEST algorithms? Does C5.0 perform better than the others?



Regards,

Indrajit



On Sat, 27 Apr 2013 01:09:19 +0530  wrote

>There isn't much out there. Quinlan didn't open source the code until about a year ago. 

I've been through the code line by line and we have a fairly descriptive summary of the model in our book (that's almost out):



  http://appliedpredictivemodeling.com/

I will say that the pruning is mostly the same as described in Quinlan's C4.5 book. The big differences in C4.5 and C5.0 are boosting and winnowing. The former is very different mechanically than gradient boosting machines and is more similar to the re-weighting approach of the original adaboost algorithm (but is still pretty different). 



I've submitted a talk on C5.0 for this year's UseR! conference. If there is enough time I will be able to go through some of the technical details.



Two other related notes: 

- the J48 implementation in Weka lacks one or two of C4.5's features that makes the results substantially different than what C4.5 would have produced  The differences are significant enough that Quinlan asked us to call the results of that function as "J48" and not "C4.5". Using C5.0 with a single tree is much similar to C4.5 than J48.



- the differences between model trees and Cubist are also substantial and largely undocumented.



HTH,

Max



 



On Thu, Apr 25, 2013 at 9:40 AM, Indrajit  Sen Gupta  wrote:



Hi All,















I am trying to use the C50 package to build classification trees in R. Unfortunately there is not enought documentation around its use. Can anyone explain to me - how to prune the decision trees?















Regards,







Indrajit











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