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_sg@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]]
>
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> and provide commented, minimal, self-contained, reproducible code.
>
--
Max
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