[R] Dump decision trees of randomForest object

[Christian Sturz]

I've tried the getTree() function and printed a decision tree with print().
However, it seems to me that it's hard to parse this representation and
translate it into equivalent if-then-else C constructs. Are there no other
ways to dump the trees into a more hierarchical form?

What do you exactly mean with the prediction in the source package?

Maybe what I wanted to ask goes in the same direction: let's say I've learned
a random forest model from a learning set. Now I would like to use it in the
future as classifier to predict new examples. How can this be done? Can I save
a learned model and than invoke R with new examples and applied them to
the saved model without again training the random forest from scratch? If so,
please give me some hints how to do that.

Regards,
Chris

-------- Original-Nachricht --------
> Datum: Thu, 9 Oct 2008 14:38:44 -0400
> Von: "Liaw, Andy" <andy_liaw at merck.com>
> An: "Christian Sturz" <linuxkaffee at gmx.net>, r-help at r-project.org
> Betreff: RE: [R] Dump decision trees of randomForest object

> See the getTree() function in the package.  Also, the source package
> contains C code that does the prediction that you may be able to work
> from.
> 
> Andy 
> 
> From: Christian Sturz
> > 
> > Hi,
> > 
> > I'm using the package randomForest to generate a classifier 
> > for the exemplary
> > iris data set:
> > 
> > data(iris)
> > iris.rf<-randomForest(Species~.,iris)
> > 
> > Is it possible to print all decision trees in the generated forest?
> > If so, can the trees be also written to disk?
> > 
> > What I actually need is to translate the decision trees in a 
> > random forest
> > into equivalent C++ if-then-else constructs to integrate them in a C++
> > project. Have this been done in the past and are there already any
> > implemented approaches/parser for that?
> > 
> > Cheers,
> > Chris
> > --
> > 
> > ______________________________________________
> > R-help at r-project.org mailing list
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> > PLEASE do read the posting guide 
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> > 
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