Re-evaluating the tree in the random forest

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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