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
> 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")
>
> f
Hi,
Let me give a simple example, assume a dataset
containing 5 instances with 1 variable and the class
label:
[x1, y]:
[0.5, A]
[3.2, B]
[4.5, B]
[1.4, C]
[1.6, C]
[1.9, C]
Assume that the randomForest algorithm create this (2
levels deep) tree:
Root node: question: x1 < 2.2?
Left terminal n
sa
Levels: setosa versicolor virginica
Note how the predictions have changed.
HTH,
Andy
> -Original Message-
> From: Martin Lam [mailto:[EMAIL PROTECTED]
> Sent: Friday, September 09, 2005 9:04 AM
> To: Liaw, Andy; r-help@stat.math.ethz.ch
> Subject: RE: [R] Re-evaluati
sicolor versicolor versicolor
> Levels: setosa versicolor virginica
> > iris.rf$forest$xbestsplit[1,1] <- 3.5
> > predict(iris.rf, iris[newiris, -5])
> [1] setosa setosa setosa
> Levels: setosa versicolor virginica
>
> Note how the predictions have changed.
>
>
