On Tue, 21 Apr 2015, Michael Grant wrote:
Dear R-Help:
From multiple sources comparing methods of tree classification and tree
regressions on various data sets, it seems that Exhaustive CHAID
(distinct from CHAID), most commonly generates the most useful tree
results and, in particular, is more effective than ctree or rpart which
are implemented in R.
I searched a bit on the web for "exhaustive CHAID" and didn't find any
convincing evidence that this method is "most commonly" the "most useful".
I doubt that such evidence exists because the methods are applicable to so
many different situations that uniformly better results are essentially
never obtained. Nevertheless, if you have references of comparison
studies, I would still be interested. Possibly these provide insight in
which situations exhaustive CHAID performs particularly well.
I see that CHAID, but not Exhaustive CHAID, is in the R-forge, and I
write to ask if there are plans to create a package which employs the
Exhaustive CHAID strategy.
I wouldn't know of any such plans. But if you want to adapt/extend the
code from the CHAID package, this is freely available.
Right now the best source I can find is in SPSS-IBM and I feel a bit
disloyal to R using it.
I wouldn't be concerned about disloyalty. If you feel that exhaustive
CHAID is the most appropriate tool for your problem and you have access to
it in SPSS, why not use it? Possibly you can also export it from SPSS and
import it into R using PMML. The "partykit" package has an example with an
imported QUEST tree from SPSS.
Michael Grant
Professor
University of Colorado Boulder
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