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 < indrajit...@rediffmail.com> 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 > > > [[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. > -- Max [[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.
