randomForest: Breiman's random forest for classification and regression
Classification and regression based on a forest of trees using random inputs.
Version: 3.9-6
Depends: R (>= 1.7.0)
Author: Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.
Maintainer: Andy Liaw <[EMAIL PROTECTED]>
which has a predict function
HTH
Gav
monkeychump wrote:
I'm interested in further understanding the differences in using many classification trees to improve classification rates. I'm also interested in finding out what I can do in R and which methods will allow prediction. Can anybody point me to a citation or discussion?
Specifically, I want to classify remotely sensed imagery where training data is extracted on class membership by the user. That training data (usually spectral bands and categorical data - e.g., soil type) is classified (using rpart for instance) and then the resulting tree is applied to the entire image. This results in a classified image that can then be checked for accuracy. Classification trees are increasingly used by the remote sensing folks but it seems like finding optimal trees is an active area of research in computational statistics.
I've seen great claims made by baggers and boosters (and just what is bumping?) of increasing classification accuracy but aside from TreeNet by Salford Systems I'm not aware of tools that can grow forests of trees that can then be used to make predictions.
Can anybody help?
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