[R] Boosting, bagging and bumping. Questions about R tools and predictions.

Gavin Simpson gavin.simpson at ucl.ac.uk
Wed Jul 23 11:28:27 CEST 2003

Take a look at the randomForest package on CRAN:

randomForest: Breiman's random forest for classification and regression

Classification and regression based on a forest of trees using random 
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 <andy_liaw at merck.com>

which has a predict function



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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Gavin Simpson                     [T] +44 (0)20 7679 5522
ENSIS Research Fellow             [F] +44 (0)20 7679 7565
ENSIS Ltd. & ECRC                 [E] gavin.simpson at ucl.ac.uk
UCL Department of Geography       [W] http://www.ucl.ac.uk/~ucfagls/cv/
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