RF forms prediction by voting. Note that each row in the output sums to
1. It says 85.7% of the trees classified the first case as "healthy"
and the other 14.3% of the trees "unhealthy". The majority (in
two-class cases like this one) wins, so the prediction is "healthy".
You can take 1 - OOB error rate as the estimate of prediction accuracy
(if you have not selected variables, e.g., using variable importance, in
building the final RF model).
Andy
________________________________
From: Chrysanthi A. [mailto:chrys...@gmail.com]
Sent: Friday, April 10, 2009 10:44 AM
To: Liaw, Andy
Cc: r-help@r-project.org
Subject: Re: [R] help with random forest package
Hi,
To be honest, I cannot really understand what is the meaning of
the votes.. For example having five samples and two classes what the
numbers below means?
healthy unhealthy
1 0.85714286 0.14285714
2 0.92857143 0.07142857
3 0.90000000 0.10000000
4 0.92857143 0.07142857
5 0.84615385 0.15384615
Suppose now, having the classification, I have an unknown sample
and according to the results that Ive got, how can I predict in which
class it belongs to? Do the votes give that prediction to us?
Also, the error is reported on the "OOB estimate of error
rate", right? For example, if we have OOB estimate of error rate:2.34%,
we can say that the prediction accuracy is approx. 97.7%? How can we
estimate the prediction accuracy?
Thanks a lot,
Chrysanthi.
2009/4/8 Liaw, Andy <andy_l...@merck.com>
I'm not quite sure what you're asking. RF predicts by
classifying the new observation using all trees in the forest, and take
plural vote. The predict() method for randomForest objects does that
for you. The getTree() function shows you what each individual tree is
like (not visually, just the underlying representation of the tree).
Andy
________________________________
From: Chrysanthi A. [mailto:chrys...@gmail.com]
Sent: Wednesday, April 08, 2009 2:56 PM
To: Liaw, Andy
Cc: r-help@r-project.org
Subject: Re: [R] help with random forest package
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_l...@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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