[R] Re-evaluating the tree in the random forest

[Liaw, Andy]

> From: Martin Lam
> 
> Dear mailinglist members,
> 
> I was wondering if there was a way to re-evaluate the
> instances of a tree (in the forest) again after I have
> manually changed a splitpoint (or split variable) of a
> decision node. Here's an illustration:
> 
> library("randomForest")
> 
> forest.rf <- randomForest(formula = Species ~ ., data
> = iris, do.trace = TRUE, ntree = 3, mtry = 2,
> norm.votes = FALSE)
> 
> # I am going to change the splitpoint of the root node
> of the first tree to 1
> forest.rf$forest$xbestsplit[1,]
> forest.rf$forest$xbestsplit[1,1] <- 1
> forest.rf$forest$xbestsplit[1,]
> 
> Because I've changed the splitpoint, some instances in
> the leafs are not supposed where they should be. Is
> there a way to reappoint them to the correct leaf?

I'm not sure what you want to do exactly, but I suspect you can use
predict().
 
> I was also wondering how I should interpret the output
> of do.trace:
> 
> ntree      OOB      1      2      3
>     1:   3.70%  0.00%  6.25%  5.88%
>     2:   3.49%  0.00%  3.85%  7.14%
>     3:   3.57%  0.00%  5.56%  5.26%
> 
> What's OOB and what does the percentages mean?

OOB stands for `Out-of-bag'.  Read up on random forests (e.g., the article
in R News) to learn about it.  Those numbers are estimated error rates.  The
`OOB' column is across all data, while the others are for the classes.

Andy

 
> Thanks in advance,
> 
> Martin
> 
> 
> 	
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