Many thanks for the reply.

So, extracting the votes, how can we clarify the classification result? If I
want to predict in which class will be included an unknown sample, what is
the rule that will give me that?

Thanks a lot,

Chrysanthi.



2009/4/8 Liaw, Andy <andy_liaw@merck.com>

> The source code of the whole package is available on CRAN.  All packages
> are submitted to CRAN is source form.
>
> There's no "rule" per se that gives the final prediction, as the final
> prediction is the result of plural vote by all trees in the forest.
>
> You may want to look at the varUsed() and getTree() functions.
>
> Andy
>
> From:  Chrysanthi A.
> > Hello,
> >
> > I am a phd student in Bioinformatics and I am using the Random Forest
> > package in order to classify my data, but I have some questions.
> > Is there a function in order to visualize the trees, so as to
> > get the rules?
> > Also, could you please provide me with the code of
> > "randomForest" function,
> > as I would like to see how it works. I was wondering if I can get the
> > classification having the most votes over all the trees in
> > the forest (the
> > final rules that will give me the final classification).
> > Also, is there a
> > possibility to get a vector with the attributes that are
> > being selected for
> > each node during the construction of each tree? I mean, that
> > I would like to
> > know the m<<M variables that are selected at each node out of
> > the M input
> > attributes.. Are they selected randomly? Is there a
> > possibility to select
> > the same variable in subsequent nodes?
> >
> > Thanks a lot,
> >
> > Chrysanthi.
> >
> >       [[alternative HTML version deleted]]
> >
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> >
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